This report estimates hydrocarbon reserves for the Alwyn North Field in the Brent East Reservoir using volumetric methods. Four wells provided field data. Well correlations showed two faults and some folds. All wells had similar water-oil contacts, suggesting reservoir continuity. Tarbert 3 had the best reservoir properties and thickness, contributing most to reserves. Estimated reserves were 19-40 million cubic meters for minimum, average, and maximum uncertainty cases. Tarbert 3 holds the major reserves in Brent East due to its thick, high-quality sandstone beds.
This document provides an overview of well log interpretation. It discusses how well logs are used to answer key questions about hydrocarbon-bearing formations like location, quantity, and producibility. The interpretation process involves identifying permeable zones using logs like SP and GR, then using resistivity and porosity logs to locate zones with hydrocarbons. Formations are further evaluated to determine porosity, fluid saturations, and other properties through techniques like density-neutron crossplots, environmental corrections, and determining formation temperature based on geothermal gradient. The goal is to locate potential producing zones and estimate hydrocarbon quantities and recoverability.
1. The document discusses various well logging tools and concepts used in petrophysical interpretation. It describes tools such as the spontaneous potential (SP) log, gamma ray (GR) log, resistivity logs including induction and lateral logs, and porosity logs.
2. Key concepts covered include the logging environment and factors that impact tool measurements like borehole conditions and mud properties. Interpretation techniques for evaluating permeable zones, formation resistivity, water saturation, and porosity are also summarized.
3. The document provides examples of using tools and concepts like the Archie formula to calculate water resistivity, determine hydrocarbon presence, and evaluate clean versus shaly formations. It also discusses corrections that must be applied to well log
This document provides information on estimating oil and gas reserves. It defines various classifications of reserves from proven to unproven, and how reserves are estimated using volumetric, material balance, and production performance methods. The key classifications discussed are proven and probable reserves, with proven reserves having a 90% certainty of recovery and probable having 50% certainty. Volumetric estimation calculates initial hydrocarbon volumes using parameters like rock volume, porosity, fluid properties, and recovery factors.
Petroleum geology refers to the application of geology to explore for and produce oil and gas. It involves analyzing seven key elements of a sedimentary basin: the source, reservoir, seal, trap, timing of maturation and migration. The source rock is evaluated for its organic content and type of kerogen. The reservoir rock is analyzed for porosity, permeability and properties. The seal is a low permeability layer that traps hydrocarbons. Traps are structural or stratigraphic features that ensure hydrocarbons remain trapped. Maturation involves the thermal history to predict hydrocarbon generation and expulsion timing. Refining derives products like gasoline and fuel oil from crude oil through cracking and distillation processes.
Formation evaluation and well log correlationSwapnil Pal
This document provides an overview of well log formation evaluation and interpretation. It discusses the basic well log tools used to measure parameters like gamma ray, resistivity, density, and neutron porosity. It describes qualitative log interpretation to identify reservoir zones, hydrocarbon-bearing zones, and fluid types. The document also covers quantitative interpretation, including calculating porosity, water saturation, and estimating hydrocarbon reserves. In conclusion, well logs provide key information for establishing the existence of producible oil and gas reservoirs, including reservoir type, thickness, porosity, permeability, and fluid saturation.
The document discusses resources and reserves in the oil and gas industry. It defines resources as total quantities of discovered and undiscovered petroleum, divided into discovered and undiscovered quantities initially in place. Reserves are classified according to certainty levels of proved, probable and possible. Reserves must be recoverable under economic conditions from known accumulations. Estimation involves volumetric, material balance and production decline analysis, considering future development projects. Regular validation through reserves reconciliation is important.
- The document discusses reservoir characteristics including rock and fluid properties that are important to understand for optimal hydrocarbon recovery. Techniques like seismic data, well logging, and testing provide valuable data to build reservoir models.
- Key rock properties that impact hydrocarbon storage and flow include porosity, permeability, and wettability. Core analysis in the lab and well logs provide data on these properties.
- Understanding fluid properties like phase behavior under reservoir conditions of pressure and temperature is also important for predicting production performance and fluid composition.
The document discusses the spontaneous potential (SP) well log. It describes how the SP log can be used to identify permeable zones, define bed boundaries, and compute shale content. It provides examples of calculating shale volume from the SP response. The document also discusses determining formation water resistivity from the SP log using both the classical method and the Silva-Bassiouni method. Additional topics covered include factors affecting the SP response, passive log correlation, zonation, and limitations of the SP log.
The document discusses well log data processing. It outlines that the main purpose of well log processing is to prepare well data for interpretation. It describes several key processing steps including data checking, depth shifting, removal of end effects, baseline shifting, rescaling, patching, splicing, value editing, tabular editing, filling gaps, and quality checking. The final step is generating a report that documents the full processing workflow and results.
Formation evaluation and well logging are processes used to determine the properties of subsurface reservoirs and identify commercially viable oil and gas fields. Key logging tools developed over time include resistivity logs in the 1920s, dipmeters in the 1940s, gamma ray and neutron logs in the 1940s, sonic logs in the 1950s, density logs in the 1960s, and logging while drilling was introduced, allowing real-time data acquisition. The document provides a historical overview of the development of various openhole well logging tools and techniques.
Reservoir rocks experience compaction when fluid is produced, causing a change in pore volume and effective stress. There are three types of compressibility - rock matrix (grain) compressibility measures change in grain volume, rock bulk compressibility measures change in total formation volume, and pore volume compressibility measures change in pore space. Accurately measuring and modeling compressibility is important for predicting changes in porosity and formation properties during production.
Here are the steps to solve these problems:
1) T at 5000 ft depth = Ts + αD
= 75 + 1.5(5000/100)
= 75 + 75
= 150 F
2) Geothermal gradient = (T2 - T1)/D2 - D1)
= (122 - 80)/2200
= 1.5 °F/100ft
So the geothermal gradient of the sandstone layer is 1.5 °F/100ft.
CMG provides three reservoir simulation software packages: IMEX, GEM, and STARS. IMEX is a black oil simulator used for conventional reservoirs. GEM is a compositional simulator that can model complex fluid behavior, including processes where inter-phase mass transfer is important. STARS is an advanced simulator used for thermal modeling and complex reactions. It is the industry standard for modeling chemical EOR processes, including polymer flooding, low salinity flooding, and microbial EOR. CMG has extensive experience using STARS to model H2S bacterial souring through history matching and forecasting. Reservoir engineers can choose the appropriate CMG simulator based on the reservoir fluids and recovery process being modeled.
History matching was performed on the Snark Field reservoir simulation model to match production data. Sensitivity analysis identified the aquifer properties and a fault transmissibility as uncertain parameters. Tuning runs modified these properties, achieving a good match. Forecasts with injection wells predicted improved production. Future analysis in OFM will identify workover candidates and explain water breakthrough.
It is a power point presentation on Gas Hydrates.
It consist of Energy Scenario, Basic Definition, methodology,
Methane Hydrate formation condition.
Future Scope
Estimation of skin factor by using pressure transientMuhamad Kurdy
Formation damage can occur during drilling, completion or production operations, reducing permeability near the wellbore. Well testing methods like drawdown and build-up tests are used to determine the skin factor, a measure of this damage. A positive skin factor indicates damage through reduced effective wellbore radius, while a negative skin indicates enhanced flow. Integrating pressure analysis with integral analysis allows consistent evaluation of well test data to derive skin factor and other reservoir properties. This project aims to evaluate skin factors from pressure transient test results using analytical and simulation methods.
Ppt 29-03-2017-reservoir characterisation and 3-d static modelling of “awe fi...Toba Awe
The document presents reservoir characterization and 3D static modeling of the Awe Field in the Niger Delta. Key findings include:
1. Petrophysical analysis of two reservoirs, G and I, across five wells found reservoir G has better porosity (29%) and permeability (262.5 mD) than reservoir I (26% porosity, 77.06 mD permeability).
2. 3D seismic interpretation identified faults within the field and mapped reservoir structure. Reservoir G contains two main faults while reservoir I has one minor fault.
3. Static modeling estimated reservoir G has a stock tank oil initially in place of 156 MMSTB while reservoir I contains 127 MMSTB, indicating reservoir G has greater
Net pay is difficult to define as it depends on factors like oil price and production, while net reservoir is easier to define as the portion of rock capable of storing hydrocarbons. Net reservoir can be determined from core data, well logs, and water saturation-height functions, and the net reservoir cutoff varies with height above the free water level. Upscaling properties for reservoir modeling requires identifying net reservoir to correctly average porosity, water saturation, and permeability over larger distances.
This document discusses principles of well logging. It describes how well logging aims to evaluate subsurface hydrocarbon accumulations through measuring properties in boreholes. It outlines different types of hydrocarbon traps and elements in a petroleum system. It then explains what a well log is and different types of logs used, including gamma ray, resistivity, sonic, and neutron logs. Gamma ray logs specifically measure natural radioactivity to distinguish between lithologies like sandstone and shale. The document provides details on interpreting gamma ray logs and calculating shale volume from gamma ray readings.
This document discusses material balance applied to oil reservoirs. It introduces the Schilthuis material balance equation, which is a basic tool for interpreting and predicting reservoir performance. The general form of the material balance equation accounts for underground withdrawal of oil and gas, expansion of oil and originally dissolved gas, expansion of any gas cap gas, and changes in hydrocarbon pore volume due to water and pore volume changes. The document provides the specific equations that make up the material balance and shows how it can be simplified for different reservoir drive mechanisms, including solution gas drive above and below the bubble point pressure. It also provides examples of calculating recovery factors and gas saturation from the material balance equation for a reservoir undergoing primary depletion by solution gas drive.
This document summarizes a research project on oil pipeline failures in Nigeria's Niger Delta region. The research aims to analyze the causes of pipeline failures in the region, examine the environmental and economic impacts, and make recommendations. It includes a background on oil pipelines and spills in the Niger Delta. The research will use both primary and secondary research methods, including questionnaires and reviewing literature. The objectives are to identify the root causes of pipeline failures in the region and challenges to pipelines, in order to make recommendations.
IRJET- Analysis of Reserve Estimation using Volumetric Method on Taq Taq Oil ...IRJET Journal
This document analyzes reserve estimation using the volumetric method on the Taq Taq oil fields in Iraq. It presents geological and petrophysical data from four reservoirs - Pilspe, Shiranish, Kometan, and Qamchaqa. The original oil in place is estimated for each reservoir using the volumetric method equation, which considers rock volume, porosity, water saturation, and formation volume factor. The estimated original oil in places for the four reservoirs are 322.296 MMSTB, 149,264,519,309 STB, 49716600472 STB, and 25429490369 STB respectively. The reserve estimates provide a basis for resource development planning of the Taq Taq field.
Oil and gas engineering involves the production of hydrocarbons from subsurface reservoirs. There are four main types: offshore, subsea, petroleum, and earth science engineering. The life cycle of an oil field consists of five stages: exploration, appraisal, development, production, and abandonment. Oil refineries process crude oil through various units to produce useful products like gasoline and diesel.
The Future of the North Sea_FINAL with Page BorderTim Shingler
The document discusses the current state of the UK Continental Shelf (UKCS) oil and gas industry. It notes that while the UKCS has been a major contributor to UK oil and gas production and government revenues for decades, production has been declining since 2000. The dramatic fall in oil prices has put pressure on operators to reduce costs and consider early decommissioning of fields. The Oil and Gas Authority is working to develop a more efficient industry and ensure the long-term future of the North Sea, but collaboration between operators and lower costs will be critical as oil prices are expected to remain low.
Brief Introduction into Oil & Gas Industry by Fidan AliyevaFidan Aliyeva
This document presents five stages of the oil field life cycle, their description and some disciplines involved as well as some general facts about the oil and gas.
The document provides an overview of the oil and gas industry, including:
- Oil formation from ancient remains of animals and plants that were subjected to heat and pressure over millions of years.
- The industry is divided into upstream (exploration and production) and downstream (processing, refining, distribution).
- The life cycle of an oil field consists of 5 stages: exploration, appraisal, development, production, and abandonment.
1) SPR has dropped plans to build an offshore wind farm near Tiree after facing years of opposition from the local community group NTA over technical and environmental concerns.
2) NTA questioned SPR's feasibility studies from the start, noting the difficult seabed geology and stormy conditions would make foundations and construction extremely challenging.
3) After many exchanges with NTA highlighting these problems, SPR has now acknowledged the project is not viable and dropped their plans, much to the relief of NTA and other opponents. However, NTA argues the decision took too long and cost too much given the obvious challenges.
Prospects of Tar Sand in Nigeria Energy Mixtheijes
In ancient times, the Elamites, Chaldeans, Akkadians, and Sumerians mined shallow deposits of asphalt, or bitumen occurring in tar sand for their own use. Mesopotamian bitumen was exported to Egypt where it was employed for various purposes, including the preservation of mummies. Tar sand had many other uses in the ancient world. It was mixed with sand and fibrous materials for use in the construction of watercourses and levees and as mortar for bricks. In Nigeria, development of heavy oil and bitumen in Tar sand reserves is increasing around the western part of the country. The increasing volume of cheaper heavy oil in the supply mix has provided an incentive for refiners to upgrade their equipment to process the poorer-quality heavier crude occurring in tar sand. The upgrading investments have helped to maintain a demand for heavy oil in spite of the declining price of conventional crude since the early 1980s. As the demand for heavy oil and synthetic crude from tar sands remains strong, heavyhydrocarbon development projects are being initiated in western part of Nigeria. In addition, unsuccessful attempts to find new giant conventional oil fields in recent years have caused some producers to turn to the marginally economic heavy hydrocarbons to replace depleted petroleum reservoirs. Bitumen development in Nigeria is also poised to become Nigerian major foreign exchange earner, second to conventional oil in the coming years.
Exhaust Analysis of Rapeseed Oil Microturbine - Tom Gaca 2004Tom Gaca
This document summarizes an exhaust analysis of a micro turbine fuelled by rapeseed oil and diesel. Key findings include:
- Emissions of CO2, CO and NO increased when running on rapeseed oil compared to diesel, but remained below recommended limits.
- Higher fuel usage, lower exhaust temperature, and higher overall efficiency were observed with rapeseed oil compared to diesel.
- The turbine ran successfully on 100% rapeseed oil with only fuel line heating added, requiring no other modifications.
- Emission levels of CO2, CO, NO and other parameters were tested and analyzed to evaluate the viability of rapeseed oil as a fuel for micro turbines.
The UKCS Continental Shelf - A Time for ActionTim Shingler
The document summarizes the current situation of the UK Continental Shelf (UKCS) and the effects of low oil prices. It notes that UKCS production and exploration activity have declined in recent years as fields mature and costs rise. The current low oil price environment poses further challenges, as many operators are struggling with high debt loads and fields becoming uneconomical at prices below $50 per barrel. The document calls for collaboration between operators and service providers to reduce costs, as well as consolidation, in order to help safeguard the long-term future of the UKCS.
The Water Management Plan at Minorca Surface Coal Mine allowed for effective monitoring and protection of on-site waterways. Key issues identified included low pH and high nickel concentrations at monitoring point CM4, indicating acid mine drainage. Increased chloride levels were also detected in site discharges. The plan enabled swift action through expanded sampling and predictive model updates to reduce risks. Adaptions to mining operations, like increased storage pond testing and dilution, additionally helped address issues. Ultimately, the interactive plan received regulator approval and ensured negligible environmental impacts.
This document summarizes SaskPower's experience developing the world's first commercial-scale integrated carbon capture and storage (ICCS) project at its Boundary Dam Power Station Unit 3. It describes SaskPower's decades-long pursuit of clean coal power technology from the 1980s onwards. Key factors in the decision to retrofit Unit 3 were continued value from existing infrastructure and revenues from sale of captured CO2, sulphuric acid, and fly ash. Major challenges included immature technology, construction complexity, and cost escalation. The project demonstrates that large-scale CCS at coal plants is viable and provides lessons for future clean coal initiatives.
Review of EOR Selection for light tight oil
Key Themes:
Upfront EOR Development Planning
Cash is king but Permeability Rules
Geology Selects Technology
Nanospheres, Steam Flooding, Misc Gas Flooding, EOR Selection Criteria
1st NRG Corp is an exploration and production company with assets in Colorado and Ohio. The document summarizes 1st NRG's recent drilling of the Townley 1S well in southeast Ohio, which encountered potential reservoirs in the Utica Shale, Beekmantown Dolomite, and Conasauga Formation. Analysis of samples from these formations indicated their hydrocarbon potential. The document also includes projections for production, revenues, costs, and cash flow from the Beekmantown and Conasauga formations over the next five years.
This document analyzes potential maintenance and supply opportunities from major oil and gas projects in Northern Australia centered around Darwin. It summarizes upcoming and existing LNG projects that will utilize Darwin as an operations hub by 2016, including the Ichthys LNG and Prelude FLNG projects. The analysis identifies capability gaps in the local supply sector that represent opportunities for companies, and provides an overview of exploration activity in key onshore basins in the Northern Territory. The goal is to help industry and government capitalize on the growth of Darwin as a major oil and gas hub.
This document provides an emergency response and disaster management plan for Essar Oil Limited. It outlines the organization's emergency response structure and procedures for various incident levels. It details Essar's oil spill response capabilities and equipment. It also describes emergency scenarios covered, communication protocols, and the roles and responsibilities of key emergency response positions. Regular mock drills are conducted to test and improve the emergency response plans.
UntitledExcessive Water Production Diagnostic and Control - Case Study Jake O...Mohanned Mahjoup
For mature fields, Excessive water production is a complex subject in the oil and gas industries and has a serious economic and environmental impact. Some argue that oil industry is effectively water industry producing oil as a secondary output. Therefore, it is important to realize the different mechanisms that causing water production to better evaluate existing situation and design the optimum solution for the problem. This paper presents the water production and management situation in Jake oilfield in the southeast of Sudan; a cumulative of 14 MMBbl of water was produced till the end of 2014, without actual plan for water management in the field, only conventional shut-off methods have been tested with no success. Based on field production data and the previously applied techniques, this work identified the sources of water problems and attempts to initialize a strategy for controlling the excessive water production in the field. The production data were analyzed and a series of diagnostic plots were presented and compared with Chan’s standard diagnostic plot. As a result, distinction between channeling and conning for each well was identified; the work shows that channeling is the main reason for water production in wells with high permeability sandstone zone while conning appears only in two wells. Finally, the wells were classified according to a risk factor and selections of the candidate wells for water shut off were presented.
Similar to Hydrocarbon reserve estimation project report for Alywn Northfield (East Brent) (20)
Natural Is The Best: Model-Agnostic Code Simplification for Pre-trained Large...YanKing2
Pre-trained Large Language Models (LLM) have achieved remarkable successes in several domains. However, code-oriented LLMs are often heavy in computational complexity, and quadratically with the length of the input code sequence. Toward simplifying the input program of an LLM, the state-of-the-art approach has the strategies to filter the input code tokens based on the attention scores given by the LLM. The decision to simplify the input program should not rely on the attention patterns of an LLM, as these patterns are influenced by both the model architecture and the pre-training dataset. Since the model and dataset are part of the solution domain, not the problem domain where the input program belongs, the outcome may differ when the model is trained on a different dataset. We propose SlimCode, a model-agnostic code simplification solution for LLMs that depends on the nature of input code tokens. As an empirical study on the LLMs including CodeBERT, CodeT5, and GPT-4 for two main tasks: code search and summarization. We reported that 1) the reduction ratio of code has a linear-like relation with the saving ratio on training time, 2) the impact of categorized tokens on code simplification can vary significantly, 3) the impact of categorized tokens on code simplification is task-specific but model-agnostic, and 4) the above findings hold for the paradigm–prompt engineering and interactive in-context learning and this study can save reduce the cost of invoking GPT-4 by 24%per API query. Importantly, SlimCode simplifies the input code with its greedy strategy and can obtain at most 133 times faster than the state-of-the-art technique with a significant improvement. This paper calls for a new direction on code-based, model-agnostic code simplification solutions to further empower LLMs.
A brief introduction to quadcopter (drone) working. It provides an overview of flight stability, dynamics, general control system block diagram, and the electronic hardware.
Presentation slide on DESIGN AND FABRICATION OF MOBILE CONTROLLED DRAINAGE.pptxEr. Kushal Ghimire
To address increased waste dumping in drains, a low-cost drainage cleaning robot controlled via a mobile app is designed to reduce human intervention and improve automation. Connected via Bluetooth, the robot’s chain circulates, moving a mesh with a lifter to carry solid waste to a bin. This project aims to clear clogs, ensure free water flow, and transform society into a cleaner, healthier environment, reducing disease spread from direct sewage contact. It’s especially effective during heavy rains with high water and garbage flow.
Slides from my talk at MinneAnalytics 2024 - June 7, 2024
https://datatech2024.sched.com/event/1eO0m/time-state-analytics-a-new-paradigm
Across many domains, we see a growing need for complex analytics to track precise metrics at Internet scale to detect issues, identify mitigations, and analyze patterns. Think about delays in airlines (Logistics), food delivery tracking (Apps), detect fraudulent transactions (Fintech), flagging computers for intrusion (Cybersecurity), device health (IoT), and many more.
For instance, at Conviva, our customers want to analyze the buffering that users on some types of devices suffer, when using a specific CDN.
We refer to such problems as Multidimensional Time-State Analytics. Time-State here refers to the stateful context-sensitive analysis over event streams needed to capture metrics of interest, in contrast to simple aggregations. Multidimensional refers to the need to run ad hoc queries to drill down into subpopulations of interest. Furthermore, we need both real-time streaming and offline retrospective analysis capabilities.
In this talk, we will share our experiences to explain why state-of-art systems offer poor abstractions to tackle such workloads and why they suffer from poor cost-performance tradeoffs and significant complexity.
We will also describe Conviva’s architectural and algorithmic efforts to tackle these challenges. We present early evidence on how raising the level of abstraction can reduce developer effort, bugs, and cloud costs by (up to) an order of magnitude, and offer a unified framework to support both streaming and retrospective analysis. We will also discuss how our ideas can be plugged into existing pipelines and how our new ``visual'' abstraction can democratize analytics across many domains and to non-programmers.
I am Dr. T.D. Shashikala, an Associate Professor in the Electronics and Communication Engineering Department at University BDT College of Engineering, Davanagere, Karnataka. I have been teaching here since 1997. I prepared this manual for the VTU MTech course in Digital Communication and Networking, focusing on the Advanced Digital Signal Processing Lab (22LDN12). Based on, 1.Digital Signal Processing: Principles, Algorithms, and Applications by John G. Proakis and Dimitris G. Manolakis, Discrete-Time Signal Processing by Alan V. Oppenheim and Ronald W. Schafer, 3.Digital Signal Processing: A Practical Guide for Engineers and Scientists" by Steven W. Smith. 4.Understanding Digital Signal Processing by Richard G. Lyons. 5.Wavelet Transforms and Time-Frequency Signal Analysis" by Lokenath Debnath . 6. MathWorks (MATLAB) - MATLAB Documentation
Top EPC companies in India - Best EPC ContractorMangeshK6
These firms are responsible for designing, procuring materials, and constructing facilities, ensuring timely delivery, and adherence to quality standards.
Here is a list of key players driving the country’s development and shaping the future of Indian infrastructure:
https://industryupdates.medium.com/top-epc-companies-in-india-f814df73c5e8
Introduction And Differences Between File System And Dbms.pptxSerendipityYoon
An introduction to file systems and a database management system. This document provides a free powerpoint presentation about the differences between a file system and database management system. Advantages and disadvantages of file system and database management system.
Girls Call Chennai 000XX00000 Provide Best And Top Girl Service And No1 in City
Hydrocarbon reserve estimation project report for Alywn Northfield (East Brent)
1. Hydrocarbon-In-Place
Estimation Project Report
for Alwyn North Field
[East Brent Panel]
APRIL 2018
GROUP 6
AJAYI OLAWALE ISAAC G2017/IPS/MSC/PPD/297
AROGUNDADE OLUSHOLA G2017/IPS/MSC/PPD/300
IBEH NAOMI GLORY G2017/IPS/MSC/PPD/304
2. Page | II
CERTIFICATION
We hereby declare that the contained report on “OOGIP Calculation & Uncertainties” was
researched, and the results thoroughly analyzed under the supervision of the project supervisor Mr.
Owil Naleimolabh and approved, having satisfied the requirements to meet project objectives for
Petroleum Engineering and Project Development (MSc.), UNIPORT/IFP School, Port-Harcourt,
Rivers state, Nigeria.
3. Page | III
ACKNOWLEDGEMENT
First of all, we would like to acknowledge the Lord above all for his guidance, protection, wisdom
and understanding throughout this project, also for the knowledge gained in the process, it has
been a blessing.
We also appreciate University of PortHarcourt and the IFP School for the opportunity to work as
a team which contributed to developing team-building spirit amongst ourselves.
We would also like to thank our project supervisor, Mr. Owil Naleimolabh for his guidance and
direction in the course of this project, and for his advice and sacrifice we want to use this medium
to appreciate the efforts we put in as a team, the drive, selflessness, and solidarity amongst us.
We also appreciate all teams in IPS Batch 15 for their help and brainstorming arguments, it helped
us all grow.
God bless you all.
4. Page | IV
NOMENCLATURE
3D 3-Dimension
Bo Oil Formation Volume Factor
DAT Depth-Area-Thickness
FWL Free Water Level
GOC Gas Oil Contact
HCIIP Hydrocarbon Initially in Place
N Ness
OIIP Oil Initially In Place
N Neutron Porosity Reading
D Density Porosity Reading
PVT Pressure-Volume-Temperature
RFT Repeat Formation Test
T Tarbert
UKCS United Kingdom Continental Shelf
WOC Water – Oil Contact
WUT Water Up to
5. Page | V
EXECUTIVE SUMMARY
Reserves estimation is one of the most essential tasks in the petroleum industry. It is the process
by which the economically recoverable hydrocarbons in a field, area, or region are evaluated
quantitatively. The aim of this project was to estimate the Hydrocarbon-In-Place for Alwyn North
field (Brent East Reservoir) using the given field data. Four wells (wells A2, N3, N1 and A4) were
drilled to help estimate hydrocarbon. The volumetric method was used for the purpose of the
estimation taking into account reserves uncertainty. Three cases of uncertainty were considered:
minimum, average and maximum case. The sandstones in the formation are acting as a source rock
for the emergence of the petroleum.
The results showed the following conclusions:
Well to well correlations showed geological structures showed the presence of two faults
and some folds.
All the wells had about the same WOC showing that the reservoir is continuous and
connected and there is a high likelihood that the faults are non-sealing.
Ness 1 was in the aquifer zone and could not be produced from.
Tarbert 3 has the highest reservoir thickness with the best reservoir petrophysical
characteristics (permeability, oil saturation and porosity) making it the most contributor to
the estimated reserve. Tarbert 2 has a lot of mica embedded in its sandstones.
T3 has the highest GRV contributed mainly by its massive sandstone beds.
The Tarbert 3 holds the major portion of the trapped hydrocarbons in Brent East. The
reserves were estimated as:
- Minimum case = 19,253,824.44 m3
- Average case = 31,421,555.11 m3
- Maximum case = 39,837,677.39 m3
6. Page | VI
TABLE OF CONTENTS
CERTIFICATION ..............................................................................................................................................II
ACKNOWLEDGEMENT................................................................................................................................. III
NOMENCLATURE ...........................................................................................................................................IV
EXECUTIVE SUMMARY.................................................................................................................................. V
TABLE OF CONTENTS ...................................................................................................................................VI
LIST OF FIGURES............................................................................................................................................IX
LIST OF TABLES............................................................................................................................................... X
1 INTRODUCTION.......................................................................................................................................1
1.1 Background ..............................................................................................................................................1
1.2 Objective and Scope..................................................................................................................................2
1.2.1 Objective.........................................................................................................................................2
1.2.2 Scope ..............................................................................................................................................2
2 DESCRIPTION OF FIELD........................................................................................................................3
2.1 Overview ..................................................................................................................................................3
2.2 Field Characteristics Tectonics...................................................................................................................4
2.2.1 Geological Setting............................................................................................................................4
2.2.2 Geological Description.....................................................................................................................5
2.2.3 Tectonics.........................................................................................................................................7
2.2.4 Sedimentology.................................................................................................................................8
2.3 Summary..................................................................................................................................................9
3 HYDROCARBON RESERVE ESTIMATION ........................................................................................ 10
3.1 Types of Reserves.................................................................................................................................... 10
3.2 Basic Definition....................................................................................................................................... 11
3.3 Methods of Estimating Reserves ............................................................................................................. 12
3.3.1 Volumetric Estimation ................................................................................................................... 13
4 METHODOLOGY.................................................................................................................................... 15
4.1 Overview of HCIIP Estimation.................................................................................................................. 15
4.2 Well logs Interpretation .......................................................................................................................... 15
4.2.1 Well to Well Surface Correlations................................................................................................... 16
4.2.2 Identification of Reservoir Zones and Thickness ............................................................................. 17
7. Page | VII
4.2.3 Identification of Fluid Contacts ...................................................................................................... 18
4.2.4 Quick-look Porosity Calculation in Water, Oil and Gas Zones .......................................................... 18
4.2.5 Determination of Resistivity of Formation Water ........................................................................... 18
4.3 Validation of Fluid Contacts Using RFT .................................................................................................... 19
4.4 Calculation of Petrophysical Properties.................................................................................................... 20
4.4.1 Net-to-Gross ratio, GN / ............................................................................................................ 20
4.4.2 Average Porosity ........................................................................................................................... 20
4.4.3 Average Initial Water Saturation.................................................................................................... 20
4.4.4 Determination of Absolute Permeability ........................................................................................ 21
4.5 Gross Rock Volume (GRV) Estimation ...................................................................................................... 21
4.5.1 DAT Procedure (Non-Eroded Zone)................................................................................................ 22
4.5.2 DAT Procedure (Eroded Zone)........................................................................................................ 22
4.6 PVT Selection- Formation Volume factor, Bo ......................................................................................... 23
4.7 Estimation of HCIIP ................................................................................................................................. 23
4.7.1 Assessment of Reservoir Uncertainties .......................................................................................... 24
4.7.2 Estimating of HCIIP Uncertainties................................................................................................... 24
5 RESULTS AND DISCUSSIONS............................................................................................................... 25
5.1 Introduction............................................................................................................................................ 25
5.2 Well Logs Interpretation ......................................................................................................................... 26
5.2.1 Well to Well Surface Correlations................................................................................................... 26
5.2.2 Logs Interpretation- Identification of Reservoir Zones.................................................................... 27
5.2.3 Resistivity and Saturation of Formation Water in the Aquifer (Formation N1)................................. 29
5.3 Validation of Fluid Contacts .................................................................................................................... 29
5.4 Petrophysical Properties and Net to Gross Ratio...................................................................................... 30
5.5 GRV Estimation....................................................................................................................................... 31
5.6 PVT Selection- Formation Volume factor, Bo ......................................................................................... 34
5.7 Estimation of HCIIP Including Uncertainties............................................................................................. 34
6 CONCLUSION.......................................................................................................................................... 36
REFERENCES................................................................................................................................................... 38
9. Page | IX
LIST OF FIGURES
Figure 1: 3D Area View of Alwyn North Field 3
Figure 2: Area Location Map of Alwyn North Field 4
Figure 3: Stratigraphy of Alwyn North Field 5
Figure 4: The Brent Geological Cross-section of Alwyn North Field 6
Figure 5: The Brent Geological Well Section of Alwyn North Field 6
Figure 6: The Cross-section through Alwyn Showing the Faults 8
Figure 7: Depositional Setting of the Brent group 9
Figure 8: Resource flow chart 11
Figure 9: Hydrocarbon Initially in Place Estimation Process 15
Figure 10: North-South direction and the West-East Directions of Correlations 16
Figure 11: Sample Phi-K for Unit N1 21
Figure 12: Eroded Surfaces 23
Figure 13: Well to Well Correlation a) North-South and b) West-East Cross- Sections 26
Figure 14: Sections of Interpreted Well Logs for Well a) A4 b) A2 c) N1 and d) N3 28
Figure 15: Pressure gradient curve for wells A4 and N3 29
Figure 16: Minimum case depth-area plot a) non-eroded zone and b) eroded zone 31
Figure 17: Average case depth-area plot a) non-eroded zone and b) eroded zone 32
Figure 18: Maximum case depth-area plot a) non-eroded zone and b) eroded zone 33
10. Page | X
LIST OF TABLES
Table 1: Data Received for Alwyn North (East Brent Reservoir).................................................1
Table 2: Reserve Estimation Methods .......................................................................................12
Table 3: Properties Obtained from Reservoir Rocks ..................................................................16
Table 4: Depth-Area data for Tarbert 3......................................................................................22
Table 5: Uncertain Reservoir Estimation Cases .........................................................................24
Table 6a: Petrophysical Properties (Porosity, Saturation, N/G, Stratigraphic Tops)....................30
Table 6b: Petrophysical Properties (Absolute Permeability) ......................................................31
Table 7: Summary of Minimum GRV .......................................................................................32
Table 8: Summary of Average GRV..........................................................................................33
Table 9: Maximum GRV...........................................................................................................33
Table 10: Differences in the PVT Study for Wells A4 and N3 ...................................................34
Table 11: Summary of Results (Minimum Case) .......................................................................34
Table 12: Summary of Results (Average Case) .........................................................................35
Table 13: Summary of Results (Maximum Case) ......................................................................35
Table 14: Well to Well Correlation Data Sheet a) North- South b) West- East...........................40
Table 15: Depth-Area Data Sheet a) Non-eroded b) Eroded ......................................................40
Table 16: Petrophysical Properties Data Sheet...........................................................................41
11. Page | 1
1 INTRODUCTION
1.1 Background
Located 340 km NE from Aberdeen and 4 and 10km south of the Stratfjord and Brent field, the
Alwyn North field was discovered in 1975 and operated by TOTAL since 1982. 2D and 3D seismic
data obtained from the field indicated the presence of a petroleum system including source rocks,
normal sealing faults with a general North-South direction, oil-bearing sandstones and a major
unconformity at the base of the cretaceous. The unconformity is related to the of the Brent
formation in the eastern Brent.
The field is divided into six panels including the Brent East and North and also comprises eight
blocks (3/4a‐ 6, 3/9a‐ 1, 2 and 3, 3/9a‐ 4 and 3/9a‐ 5 and 3/4a‐ 8). The first well (3/4a‐ 6) drilled
in 1975 with oil in the Brent group and condensate gas in the Strafjord sandstone. Five appraisal
wells; A1, A2, A3, A4 and A5 in block 3/9 were drilled between 1971 and 1982 to further confirm
the presence of oil and condensate gas.
It has been decided that the Hydrocarbon Initially in Place (HCIIP) will be estimated for the East
Brent area. The volumetric method will be used in the estimation. This is to illustrate the concept
of volumetric reserves estimate in this course taking into consideration uncertainties.
To better understand the formation and obtain data that would be further used in reservoir and PVT
studies, core and plug samples were collected from the drilled wells and have been analyzed in the
laboratory. The wells were also logged to obtain well log data. Detailed results obtained from the
analysis were received and will be used to estimate the reserve. Table 1 contains the data received.
Table 1: Data Received for Alwyn North (East Brent Reservoir)
S/N Title Description
1 Annex 1 Phi-k cross plots for Ness and Tarberts
2 Annex 4 Pressure measurement synthesis
3 Annex 5 PVT study for wells N3 and A4
4 Annex 6 N1, N3 vertical and deviated depths
5 Annex 7 Documentation for log interpretation
12. Page | 2
1.2 Objective and Scope
1.2.1 Objective
The objective of this Engineering project is to estimate the hydrocarbons in place (HCIIP) for the
Alwyn North Field (Brent East Reservoir) by undertaking the following specific activities:
● Interprete its well logs
● Calculate the petrophysical properties
● Estimate gross rock volume (GRV)
● Selecting the formation volume factor (FVF)
● Estimating the HCIIP while considering reservoir uncertainties
1.2.2 Scope
The study was limited to the East panel of the Alwyn North field.
13. Page | 3
2 DESCRIPTION OF FIELD
2.1 Overview
The Alwyn North Field was discovered in 1974 in the South Eastern part of the East Shetland
Basin in the UK North-sea, about 140 km East of the near most Shetland Island and about 400 km
North East of Aberdeen. The Alwyn field lie respectively 4 and 10 km south of Strathspey and
Brent field, 7 km east of Ninian field, and 10 km north of Dunbar field (see field localization map
below). The water depth is around 130 m. The field is in the UKCS Block 3/9 and extends
northward into the Block 3/4. The location map and 3D view of the area is shown in figure 1 and
2 respectively.
Figure 1: 3D Area View of Alwyn North Field
14. Page | 4
Figure 2: Area Location Map of Alwyn North Field
2.2 Field Characteristics Tectonics
Tectonics played a significant role on the structure of ALWYN North field. Tensional movements
leading to the development of the Viking Graben from the lower Permian times to Upper Jurassic
generated a complex fault pattern. Several seismic data acquisition programs were carried out: 2D
seismic in 1974 and 1977, and 3D in 1980/81. Seismic data analysis indicates that the oil bearing
sands are controlled on one hand by normal sealing faults with a general North-South direction,
on the other hand by a major unconformity at the base of Cretaceous. This unconformity is related
to erosion of the Brent formation in the eastern part of ALWYN North field. In a bid to explore
the Alwyn North field a thorough geological description of the field is necessary to ensure
complete understanding of the geology of the area. The geological setting, sedimentology and
other related aspects of the field are described in this section
2.2.1 Geological Setting
The Brent formation was deposited in a deltaic and shallow marine environment during the Middle
Jurassic period. The Statfjord formation was deposited in a fluvial and shallow marine
environment during the Lower Jurassic period. Each panel has several pre-cretaceous tilted blocks
(see Figure 3 below). The cap-rock is made of three on lapping shaly formations:
15. Page | 5
Heather formation: marine transgressive shales with thin limestone stringers, which is
deposited after the tectonic activity.
Kimmeridge clay thick in the West, thin in the East, which is the main hydrocarbon source
rock
Thick cretaceous sequence
Figure 3: Stratigraphy of Alwyn North Field
ALWYN North reservoirs were relatively unaffected by diagenesis due probably to an
early hydrocarbon impregnation RFT shows that each panel had its own pressure regime. Water-
oil contacts were identified at different depth. All the panels were independent from the other.
2.2.2 Geological Description
The structure of Alwyn Brent East Block was generally an eroded monoclonal, with Base
Cretaceous Unconformity (BCU) setting east and south limit, Spinal Fault setting west limit
(separating Brent east from north and central west blocks), and a fault with sometimes very small
throw setting north limit. East structure under BCU is quite complicated, and described under the
generic term of slumps (linked to gravitational collapse of head blocks during Cretaceous erosion
– similar as ones encountered in Brent field). In the Brent East panel, the oil zone is in a
stratigraphic trap as shown below created by the erosion unconformity to the east, by a north -
16. Page | 6
south fault to the west (between A-1 and A-2 wells) and by a tranverse fault to the north. The Brent
Geological Cross section is shown below.
Figure 4: The Brent Geological Cross-section of Alwyn North Field
The Brent geological well section is shown in Figure 5.
Figure 5: The Brent Geological Well Section of Alwyn North Field
17. Page | 7
2.2.3 Tectonics
Several seismic data acquisition programs were carried out: 2D seismic in 1974 and 1977, and 3D
in 1980/81. Seismic data analysis indicates that the oil bearing sands are controlled on one hand
by normal sealing faults with a general North-South direction, on the other hand by a major
unconformity at the base of Cretaceous. This unconformity is related to erosion of the Brent
formation in the eastern part of ALWYN North field following the seismic interpretation, ALWYN
North field was divided into the following panels:
Brent North
Brent Northwest.
Brent Southwest.
Brent East.
Statfjord
Triassic
The first four panels are oil bearing within the Brent. The Statfjord formation is a condensate gas
reservoir with the Brent completely eroded. The underlying Triassic is gas bearing.
18. Page | 8
Figure 6: The Cross-section through Alwyn Showing the Faults
2.2.4 Sedimentology
The Brent group is divided into three main units:
The Lower Brent (Broom, Rannoch and Etive formations),
The Middle Brent (Ness formations), and
The Upper Brent (Tarbert formations).
The last two are the only oil-bearing formations in the Brent East panel. The Lower Brent
formation was deposited in a shore-face (Rannoch) to coastal barrier (Etive) environment. The
clastic reservoir is made of transgressive sandstone (Broom) and prograding sandstones
(Rannochand Etive). Thus, the petrophysical properties range from low to medium permeability.
This unit does not contain any oil in the Brent East reservoir.
The Middle Brent formation was deposited in a deltaic to alluvial plain, Ness 1(N1) and lagoon to
lower delta plain, Ness 2 (N 2) environment. Thus sandstones are inter-bedded with clay and coal.
In general, Ness 1 unit has poorer petrophysical characteristics than Ness 2 unit and its oil-bearing
leg is much lower especially to the East of the reservoir.
The Upper Brent was deposited in a prograding lower shoreface environment. Three different
types of sandstone are identified. At the top Tarbert 3 (T3) are massive sands with very good
reservoir characteristics. This is the main oil bearing unit in the Brent East reservoir. Below Tarbert
2 (T2), there are mica-rich sandstones with lower permeability. These mica-rich sandstones exhibit
a high natural radioactivity. The base of the Tarbert formation, Tarbert 1 (T1), is very similar to
the top sandstone. Despite its lower average permeability, Tarbert 2 unit is not considered as a
permeability barrier.
19. Page | 9
Figure 7: Depositional Setting of the Brent group
2.3 Summary
To summarize, Tarbert can be described as massive shore face sands with excellent petro-physical
properties, well connected throughout the field and may be even regionally, communicating
partially with Upper Ness fluviatile system which is isolated from Lower Ness. Base Brent Etive
and Rannoch are better quality reservoirs, but mainly water bearing in Brent East Block.
Considering the small oil content in Ness 1, this unit is neglected in the reservoir model. Thus, the
reservoir model focuses on the Ness 2 and Tarbert 1, 2 and 3 units. The Brent East reservoir of
Alwyn North was characterized using data from two of the original vertical appraisal wells (3/9A-
2, 3/9A-4) and two new deviated delineation wells (N1 and N3). N3 characterized the northern
part of the field where an important oil leg was confirmed mainly in the Tarbert units. N1 located
to the West did not produce any oil and only encountered the aquifer, which does seem to be
active. The water salinity in the reservoir is about 17,000ppm.
20. Page | 10
3 HYDROCARBON RESERVE ESTIMATION
Reserves are estimated volumes of crude oil, condensate, natural gas, natural gas liquids, and
associated substances anticipated to be commercially recoverable from known accumulations from
a given date forward, under existing economic conditions, by established operating practices, and
under current government regulations. Understanding the recoverable oil & gas reserves is
important when trying to establish their present and future value. The definition of reserves takes
into account the technical and commercial certainty of extraction using existing technology.
3.1 Types of Reserves
The Society of Petroleum Engineers (SPE) categorizes reserves into two main types based on its
degree of uncertainty using the current economic conditions including prices and costs and the
available technology prevailing at the time of the estimate (see figure 8):
1) Proved Reserves; 90% certainty of commercial extraction
2) Unproved Reserve; which is further divided as:
Probable Reserves, 50% certainty of commercial extraction
Possible Reserves, 10% certainty of commercial extraction.
The range of uncertainty reflects a reasonable range of estimated potentially recoverable volumes
for an individual accumulation or a project. In the case of reserves, this range of uncertainty can
be reflected in estimates for
- Proved reserves (1P),
- Proved + probable reserves (2P),
- Proved plus probable plus possible reserves (3P) scenarios.
Other categories such as low estimate, best (or average) estimate, and high estimate are also
recommended.
Total Oil and Gas Resource
UndiscoveredDiscovered
21. Page | 11
Figure 8: Resource flow chart
3.2 Basic Definition
For a better understanding on estimating reserves, a few important terms require definition.
1) Original oil in place (OOIP) and original gas in place (OGIP): The total volume of
hydrocarbon stored in a reservoir prior to production. Reserves or recoverable reserves are
the volume of hydrocarbons that can be profitably extracted from a reservoir using existing
technology.
2) Resources: reserves plus all other hydrocarbons that may eventually become producible;
this includes known oil and gas deposits present that cannot be technologically or
economically recovered (OOIP and OGIP) as well as other undiscovered potential reserves.
22. Page | 12
3.3 Methods of Estimating Reserves
Estimating hydrocarbon reserves is a complex process that involves integrating geological and
engineering data. Depending on the amount and quality of data available, one or more of the
following methods may be used to estimate reserves:
1) Volumetric
2) Material balance
3) Production history
4) Analogy
These methods are summarized in Table 2.
Table 2: Reserve Estimation Methods
S/N
Method Application Accuracy
1
Volumetric
OOIP, OGIP, recoverable reserves.
Use early in life of field.
Dependent on quality of
reservoir description. Reserves
estimates often high because this
method does not consider
problems of reservoir
heterogeneity.
2
Material balance
OOIP, OGIP (assumes adequate
production history available),
recoverable reserves
(assumes OOIP and OGIP known).
Use in a mature field with abundant
geological, petrophysical, and
engineering data.
Highly dependent on quality of
reservoir description and
amount of production data
available. Reserve estimates
variable.
3 Production
history
Recoverable reserves. Use after a
moderate amount of production data
is available.
Dependent on amount of
production history available.
Reserve estimates tend to be
realistic.
4
Analogy
OOIP, OGIP, recoverable reserves.
Use early in exploration and initial
field development.
Highly dependent on similarity
of reservoir characteristics.
Reserve estimates are often very
general.
The volumetric method is discussed in the next section.
23. Page | 13
3.3.1 Volumetric Estimation
Volumetric estimates of HCIIP are based on a geological model that geometrically describes the
volume of hydrocarbons in the reservoir. However, due mainly to the decrease in temperature and
pressure from the reservoir to the surface, dissolved gases in oil evolves and expands at the surface
thereby occupying larger volume in stock tanks. This necessitates correcting subsurface volumes
to standard units of volume measured at surface or stock tank conditions. The basic volumetric
equation (in field units) used is:
𝑂𝐼𝐼𝑃 = 7,758𝐴ℎ𝛷(1 − 𝑆 𝑤)/𝐵 𝑜𝑖 (1)
𝑂𝐺𝐼𝑃 = 43,560𝐴ℎ𝛷(1 − 𝑆 𝑤)/𝐵 𝑔𝑖 (2)
Where; OIIP = Oil Initially in Place (STB)
OGIP = Gas Initially in Place (SCF)
A = Area of reservoir (acres) obtained from map data
h = Height or thickness of pay zone (ft) obtained from log and/or core data
𝛷 = Porosity obtained from log and/or core data
𝑆 𝑤 = Connate water saturation obtained from log and/or core data
𝐵 𝑤𝑖, 𝐵 𝑔𝑖 = Formation volume factor for oil (reservoir bbl/STB) and gas (reservoir bbl/SCF)
respectively at initial conditions from lab data;
Recoverable reserves are a fraction of the OOIP or OGIP and are dependent on the efficiency of
the reservoir drive mechanism. The basic equation used to calculate recoverable oil reserves is:
𝑅𝑒𝑐𝑜𝑣𝑒𝑟𝑎𝑏𝑙𝑒 𝑂𝑖𝑙 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑠 ( 𝑠𝑡𝑏) = 𝐻𝐶𝐼𝑃 × 𝑅𝐹 (3)
Where; RF = Recovery factor
HCIIP = OIIP or GIIP
The RF is dependent on the method of recovery used in producing the hydrocarbon. It is the sum
of the primary and secondary recovery. The primary recovery factor is estimated from the type of
drive mechanism. The secondary recovery factor, RFS, is given by:
24. Page | 14
𝑅𝐹𝑠 = 𝐸 𝐷 × 𝐸𝐴 × 𝐸𝑣 (4)
Where;
ED = displacement efficiency
EA = areal sweep efficiency
EV = vertical sweep efficiency
These efficiency terms are influenced by such factors as residual oil saturation, relative
permeability, reservoir heterogeneity, and operational limitations that govern reservoir production
and management. Thus, it is difficult to calculate the recovery factor directly using these terms.
Other methods such as the decline curves are often applied.
25. Page | 15
4 METHODOLOGY
4.1 Overview of HCIIP Estimation
The front-end activities that provided data and information used in determining the OIIP are
summarized by the flow schematic in Figure 9. The approach taken to complete each activity is
described in the sections following the figure.
Figure 9: Hydrocarbon Initially in Place Estimation Process
4.2 Well logs Interpretation
Logs data obtained from the various wells were analyzed for interpretation. . The purpose of
interpreting the logs was to:
Interpretation of
Well Logs
Calculation of
Petrophysical properties
(K, N/G, and )
Validation of Fluid
Contacts Using RFT
Estimation of Gross
Rock Volume
PVT Selection - Bo
Estimation of HCIIP
Well to Well Surface
Correlations
Identification of
Reservoir Zones
Identification of
Fluid Contacts
Quick-look Porosity
Calculation
Determination of
formation Water
Resistivity
26. Page | 16
a) Perform well to well surface correlations.
b) Identify the reservoir rocks and obtain a number of physical parameters related to both its
geological and petrophysical properties. Parameters obtained from this process are listed
in table 3.
c) Identify, characterize and quantify the fluids present in the reservoir rocks.
Table 3: Properties Obtained from Reservoir Rocks
S/N Parameters (Rock and fluid)
1 Lithology
2 Reservoir zones and thickness
3 Fluid contacts- water-oil, gas-oil and gas-water
4 Fluids present (oil, water and gas) and net pay zones
5 Resistivity
6 Porosity
4.2.1 Well to Well Surface Correlations
Well to well correlation takes into account the various sand surfaces in each well with the
isobaths reading for correlating the surfaces. It is a structured scheme to define reservoir
architecture and quality and the relationships of the depositions in time. This is within the
context of sequence stratigraphy. The correlations were done in both the North-South
direction and the West-East directions crossing through faults and the wells as seen in
figure 10.
Figure 10: North-South direction and the West-East Directions of Correlations
27. Page | 17
Well to well correlation was also used to show the sequence stratigraphic surfaces (tops)
of all the formations in the different wells in Alwyn North field. The stratigraphic top of
well A4 was provided. Hence, all others wells were matched to those of well A4. The
surfaces were identified based on a number of criteria which included the WUT, WOC,
GOC gamma ray and resistivity.
4.2.2 Identification of Reservoir Zones and Thickness
Reservoir zones were identified by identifying and eliminating areas considered as non-
reservoir zones namely:
- Shale
- Tight formation
- Salt
- Coals
The processes involved in identifying each are listed below.
I. Shale
- Presence of caving as observed from deviation between the caliper log and bit size.
- Highly radioactive sections with gamma ray greater than 70 API.
- No invasion: Low resistivity of less than 20 ohm-m and the resistivity readings are
close to each other.
- High Neutron values of 30%.
- Large Neutron-Density separation.
- Shale base line indicator from Spontaneous Potential readings.
II. Tight formations
- Caliper close to Bit size
- Low Gamma Ray values of less than 30 API
- High resistivity values of greater than 200 ohm-m and resistivity readings close to each
other.
- Density, Neutron, Sonic values close to matrix reference values.
- Low Neutron and Sonic readings but high density readings
28. Page | 18
III. Other non-reservoir sections (salt, coal etc.)
- Caliper close to bit size, but caving can be observed in Salt
- Low gamma ray values for Halite and Anhydrite
- Very high resistivity values
The net thickness of the reservoir zones, uh are measured and recorded for each well.
4.2.3 Identification of Fluid Contacts
- Water - Oil contact were identified using the resistivity overlay technique
- Gas – Oil contact were identified using Density-Neutron separation (Gas effect)
4.2.4 Quick-look Porosity Calculation in Water, Oil and Gas Zones
Porosity was calculated using the formula below:
Oil and water zones:
2
DN
(5)
Gas zones:
4
3 ND
(6)
Where; N = Neutron Porosity reading
D = Density Porosity reading
4.2.5 Determination of Resistivity of Formation Water
Resistivity values were obtained from the LLD and LLS logs. LLD log represents the
water resistivity in non-invaded zone while LLS log provided the resistivity of formation
water in the flushed zone.
Archie’s formula I and II was used in calculating the resistivity of water in the water zone
and saturation of water in all the various reservoir zones.
Rxo
Rt
RmfRw (7)
Where; Rw= Resistivity of formation water in the aquifer, Ωm
Rmf = Resistivity of mud filtrate, Ωm
Rt = Resistivity of non-invaded zone, Ωm
Rxo= Resistivity of flushed zone, Ωm
29. Page | 19
Computation of fluid saturations in various zones:
n
t
w
mw
R
R
S
a
(8)
whc SS 1
Where: wS = Water saturation, s.u
hcS = Hydrocarbon saturation, s.u
Rt = Resistivity of non-invaded zone, Ωm
= Porosity, p.u
m = cementation factor = 2 for clean sandstone
n = saturation exponent =1 for clean sandstone
NB: it is assumed that the formation sandstones are clean for Archie’s formula to be
applicable. Resistivity of formation water is calculated using Archie’s formular (equation
(8)). Saturation of water is 1. Rt is read off from the logs.
4.3 Validation of Fluid Contacts Using RFT
The Repeat Formation Tester (RFT) was used to validate the fluid contacts obtained from the well
logs.
Formation pressure changes vertically with depth as the fluid in the wellbore changes. The change
in the pressure gradients is the basis for determining free water level (FWL) in the wellbore. The
formation pressure versus depth data was received for well A-2, A-4 and N3.
Procedure
- Formation pressure was plotted against TVDSS for each well.
- The depths where the characteristic pressure gradient changes were recorded
Assumption: The OWC may vary from the FWL. However, it is assumed that the FWL=WOC.
30. Page | 20
4.4 Calculation of Petrophysical Properties
4.4.1 Net-to-Gross ratio, GN /
This is the ratio of the net pay thickness (corresponding to the net pay zone) to the gross
sand thickness of a geological unit. It was obtained using equation 5 below.
t
u
h
h
GN / (9)
Where; uh = Net pay thickness
th = Gross sand thickness
Net pay zones cut-offs are assigned based on the following:
- Oil saturation greater than or equal to 0.3.
- Reservoir thickness greater than 1m
- Porosity greater than 1%
- Permeability values greater than 1mD.
4.4.2 Average Porosity
The thickness weighted average porosity equation was used in obtaining the average
porosity over the net reservoir zones. For each geological unit, the average porosity Φ is
given by:
ui
ui
h
hΦ
Φ i
(10)
Where; iΦ = porosity of each sub-reservoir units in the geological unit
uih = thickness of the sub-reservoir unit
4.4.3 Average Initial Water Saturation
Like the porosity, the average initial water saturation, wiS for each reservoir zones in a
geological unit is given by:
31. Page | 21
ui
ui
hΦ
hΦ
i
iwii
wi
S
S (11)
Where; wiiS = porosity of each sub-reservoir units in the geological unit
uih = thickness of the sub-reservoir unit
iΦ = porosity of each sub-reservoir units in the geological unit
4.4.4 Determination of Absolute Permeability
The absolute permeability for each sand layer was estimated using the permeability-
porosity data for each sub-reservoir unit (see sample Annex 1 document in figure 11). The
value of average porosity obtained from section 4.2.4 was used to read off the permeability
in each case.
Figure 11: Sample Phi-K for Unit N1
4.5 Gross Rock Volume (GRV) Estimation
GRV is the volume enclosed by the top and bottom surface of a reservoir and above the water
contact. The GRV of Alwyn North was estimated using the traditional depth-area- thickness
(DAT) method. Table 4 is the depth-area-thickness data received for tops of T3.
32. Page | 22
Table 4: Depth-Area data for Tarbert 3
S/N Depth (Top of T3)
(m3
)
Area
(Km2
)
1 3,120 0.06
2 3,140 0.73
3 3,160 2.09
4 3,180 3.32
5 3,200 5.77
6 3,220 8.91
7 3231 11.33
The non-eroded zone occupies 55% of the total area.
4.5.1 DAT Procedure (Non-Eroded Zone)
- The depths of surfaces T2, T1, N2 and N1 corresponding to each given depths of T3
were calculated.
𝑍 = 𝑍𝑜 + 𝑡ℎ𝑖𝑐𝑘𝑛𝑒𝑠𝑠 𝑜𝑓 𝑏𝑒𝑑 𝑎𝑏𝑜𝑣𝑒 𝑍 (12)
Where; 𝐷( 𝑇3)= Depth (Top of T3)
- The areas occupied for the eroded zone were calculated by:
𝑁𝑜𝑛 − 𝐸𝑟𝑜𝑑𝑒𝑑 𝐴𝑟𝑒𝑎 = 0.55 × 𝐴𝑟𝑒𝑎 (13)
- The depths of all the surfaces were plotted against the non-eroded areas.
- The volume occupied between two surfaces was estimated for each geological unit.
4.5.2 DAT Procedure (Eroded Zone)
- The areas occupied for the eroded zone were calculated by:
𝐸𝑟𝑜𝑑𝑒𝑑 𝐴𝑟𝑒𝑎 = 0.45 × 𝐴𝑟𝑒𝑎 (14)
- The depths of all the surfaces were plotted against the eroded areas.
𝑍 = 𝑍𝑜 +
𝐻𝑜−𝐻
2
(15)
Where; 𝑍𝑜 = Depth (Top of T3)
𝐻𝑜 = thickness of bed when T2, T1, N2 or N1 from T3
𝐻 = thickness of bed when T2, T1, N2 or N1 from T3 is zero (see figure 12)
33. Page | 23
Figure 12: Eroded Surfaces
4.6 PVT Selection- Formation Volume factor, Bo
The formation oil volume factor is a ratio that relates the volume of oil within the reservoir to the
volume at standard conditions. The PVT study report (Annex 5-2 and Annex 5-3) provided
contains the laboratory methods used in determining Bo on well A4 and N3 respectively. They
are:
The constant composition study
The multi-stage separator or process test experiment
The differential vaporization experiment
The reservoir fluid conditions temperature and pressure are 112o
C and 445.5 bar respectively. For
a better representation of Bo for the estimation of HCIIP, the composite Bo value drawn from the
constant composition study and the process test experiment were used. Since the reservoir pressure
is higher than the saturated pressure (270.1 bar), the composite Bo equation used is given by:
𝐵𝑜𝑐 =
𝑉(𝑃)
𝑉(𝑃𝑠𝑎𝑡)
× 𝐵𝑜𝑝( 𝑃𝑠𝑎𝑡) (16)
Where; 𝐵𝑜𝑝( 𝑃𝑠𝑎𝑡) = Formation volume factor of oil at saturated pressure
𝑉( 𝑃) = Volume of oil at reservoir pressure
𝑉( 𝑃𝑠𝑎𝑡) = Volume of oil at saturated pressure
Because of the absence of a PVT study report for the other wells, it is assumed that the composite
Bo for well A4 is same for all the other wells.
4.7 Estimation of HCIIP
For an oil reservoir, the Oil Initially in Place (OIIP) is given by:
34. Page | 24
𝑂𝐼𝐼𝑃 = 𝐺𝑅𝑉 × 𝛷 × 𝑆𝑜 ×
𝑁
𝐺
×
1
𝐵𝑜
(17)
For a gas reservoir, the Gas Initially in Place (GIIP) is given by:
𝐺𝐼𝐼𝑃 = 𝐺𝑅𝑉 × 𝛷 × 𝑆𝑔 ×
𝑁
𝐺
×
1
𝐵𝑜
(18)
Where; 𝑆𝑜 = Oil saturation given by:
= 1 − 𝑆𝑤
𝑆𝑔 = Gas saturation given by:
= 1 − 𝑆𝑤 − 𝑆𝑜
4.7.1 Assessment of Reservoir Uncertainties
Reservoir uncertainty is the variation of HCIIP in the range of possible outcomes. Every step taken
in estimating the HCIIP, starting from the seismic interpretation, has a level of uncertainty
attached to it. Hence, it is imperative that these uncertainties are taken into consideration before
good developmental decisions are made.
Geological uncertainties evaluation includes structural uncertainties and some dynamic
uncertainties.
4.7.2 Estimating of HCIIP Uncertainties
For this study, the deterministic method was used in estimating the HCIIP uncertainties. Three
cases were considered namely:
1) Optimistic hypotheses analyzed for maximum case (P90)
2) Reasonable hypotheses analyzed for average case (P50)
3) Pessimistic hypotheses analyzed for minimum case (P10).
The main geological uncertain parameters affected by these methods are structural uncertainties
(GRV) and the static uncertainties (N/G, 𝛷, wiS )
Table 5: Uncertain Reservoir Estimation Cases
Case Bed Thickness Porosity Water Saturation GRV
35. Page | 25
Maximum
case
Maximum value Maximum
value
Minimum value Maximum bed
thickness
Average case Average value Average Average value Average bed
thickness
Minimum
case
Minimum value Minimum
value
Maximum value Minimum bed
thickness
5 RESULTS AND DISCUSSIONS
5.1 Introduction
The following sections present the main results obtained from the determination of the HCIIP for
the Alwyn North field. More comprehensive results can be found in the Appendix section of this
report.
36. Page | 26
5.2 Well Logs Interpretation
5.2.1 Well to Well Surface Correlations
(a)
(b)
Figure 13: Well to Well Correlation a) North-South and b) West-East Cross- Sections
Figures 13 shows that there are many geological structures present in the formations of the Brent
East Group such as tilted heavy faulting which is due to the deposition of the sandstone formation
in the early Jurassic age and creating of the North Viking Graben.
The WOC line gives a clear picture of the layers that are in the aquifer.
The depths of the stratigraphic tops can be found in table 6a.
Fault
Eroded surface
37. Page | 27
5.2.2 Logs Interpretation- Identification of Reservoir Zones
(a) (b)
38. Page | 28
(c) (d)
Figure 14: Sections of Interpreted Well Logs for Well a) A4 b) A2 c) N1 and d) N3
The green shadings indicate non-reservoir zones such as shale and micaceous. Red shading
indicates the non-shaly and non- micaceous layers.
T3 are massive sands with very good reservoir characteristics. This is the main oil bearing unit in
the Brent East reservoir. In T2, there are mica-rich sandstones with lower permeability. These
mica-rich sandstones exhibit a high natural radioactivity. Ness 2 formation is characterized by
numerous intercalations of shale, mudstones, coal and sandstones.
At a depth of about 3,120m, there is an observed deviation of RHOB, NPH curves indicating a
change in formation fluid, thereby confirming a WOC. This value conforms to 3,231m stated in
the given data.
In N1, the resistivity and density is almost constant. Since it is below the identified WOC, it can
thus be concluded that this formation is an aquifer. Hence it does not contribute to the OIIP
estimate.
39. Page | 29
Ness 1 unit has poorer petrophysical characteristics than Ness 2 unit and its oil-bearing leg is much
lower especially to the East of the reservoir.
No GOC was observed.
5.2.3 Resistivity and Saturation of Formation Water in the Aquifer (Formation N1)
In the water region, the saturation is assumed to be 1 as it the region was majorly water. Using
equation (8), the resistivity of water in the aquifer was estimated as 0.126. See Appendix II for
detailed calculations.
The average resistivity and saturation values for all zones are listed in the attached excel file in
Appendix III.
5.3 Validation of Fluid Contacts
Figure 15: Pressure gradient curve for wells A4 and N3
The pressure- gradient curves of wells A4 and N3 indicated a change in gradient. The region with
the higher gradient of 17.11bar/m is the oil and the lower gradient of 9.32 bar/m indicates water.
WOC
40. Page | 30
The pressure gradient changes at a depth of about 3,232.5m which indicates the WOC. The WOC
depth is approximately equal ad hence validate the WOC obtained from logs.
5.4 Petrophysical Properties and Net to Gross Ratio
Table 6a: Petrophysical Properties (Porosity, Saturation, N/G, Stratigraphic Tops)
The table above shows some of the results obtained from the well to well correlations exercise: the
top of the bed layers to be precise. In all the wells, T3 have the highest reservoir thickness with
good petrophysical characteristics i.e. high porosity (between 22.59 – 26 p.u) and highest oil
saturation. This makes it the bed with the highest oil leg. This is due to its high permeability as
indicated by the permeability data.
Because N1 is an aquifer with no reservoir, its thickness does not contribute to the overall
estimation of OIIP. Its water however will provide natural drive that will be useful during
production.
41. Page | 31
Table 6b: Petrophysical Properties (Absolute Permeability)
Table 6b shows the average permeability for each layer. The permeabilities range from 50-2600
mD. T3 and N2 showed similar permeability. T2 and T1 have equal lower permeability of 50 mD
mainly due to the presence of micaceous sandstones. The base of T2 and top of T3 has similar
permeability of 300mD. Despite its lower average permeability, T2 unit is not considered as a
permeability barrier.
5.5 GRV Estimation
1) Minimum Case
(a) (b)
Figure 16: Minimum case depth-area plot a) non-eroded zone and b) eroded zone
42. Page | 32
Table 7: Summary of Minimum GRV
Sand Sand GRV Total Sand GRV
Non- Eroded Eroded
T3 160,000,000 7,200,000 167,200,000
T2 16,000,000 3,600,000 19,600,000
T1 0 0 0
N2 48,000,000 31,200,000 79,200,000
Total Minimum GRV 266,000,000
2) Average Case
(a) (b)
Figure 17: Average case depth-area plot a) non-eroded zone and b) eroded zone
43. Page | 33
Table 8: Summary of Average GRV
Sand Sand GRV Total Sand GRV
Non- Eroded Eroded
T3 194,500,000 206,500,000 206,500,000
T2 18,000,000 27,800,000 27,800,000
T1 7,000,000 11,800,000 11,800,000
N2 17,000,000 69,000,000 69,000,000
Total Average GRV 315,100,000
3) Maximum Case
(a) (b)
Figure 18: Maximum case depth-area plot a) non-eroded zone and b) eroded zone
Table 9: Maximum GRV
Sand Sand GRV Total Sand GRV
Non- Eroded Eroded
T3 220,000,000 11,200,000 191,200,000
T2 40,000,000 16,000,000 44,000,000
T1 12,000,000 12,800,000 20,800,000
N2 4,000,000 59,200,000 63,200,000
Total Maximum GRV 319,200,000
44. Page | 34
Figures 16 – 18 gives a clear picture of formations that have some of its thickness below the WOC.
In all cases, T3 was above the WOC. The GRV was calculated only for the parts of the reservoir
above the WOC.
Again, T3 has the highest GRV due to it having the highest bed thickness above WOC.
5.6 PVT Selection- Formation Volume factor, Bo
The composite Bo was calculated are 1.6614 and 1.5 for well N3 (See Appendix I for detailed
calculation). The variation in their results is due mainly to the variation in the conditions under
which the experiments were conducted. Table 10 contains the major differences between each
study that may have contributed to the differences in the composite Bo..
Table 10: Differences in the PVT Study for Wells A4 and N3
S/N Well A4 Well N3
1 Study was conducted in 1980 Study was conducted in 1987
2 Three-stage process condition
separation test was conducted.
Two-stage process condition separation
test was conducted.
The composite Bo was chosen for the calculation of the OIIP due to the following reasons:
1. Because well A4 was drilled before N3, the PVT result gives more representation of the
reservoir oil in its original state.
2. 3-stage separation gives better separation than the two stage separation. hence, a better
value of Bo.
5.7 Estimation of HCIIP Including Uncertainties
1) Minimum Case (P10)
Table 11: Summary of Results (Minimum Case)
45. Page | 35
2) Average Case (P50)
Table 12: Summary of Results (Average Case)
3) Maximum Case (P90)
Table 13: Summary of Results (Maximum Case)
46. Page | 36
6 CONCLUSION
HCIIP estimation is the cornerstone of and exploration and production process. Before effective
developmental decisions can be made, it is necessary that uncertainties in estimating the HCIIP
are taken into consideration at every step of the process.
The Volumetric method was used to estimate the HCIIP for Alwyn North field. Five parameters
were obtained namely:
1) Gross rock volume obtained from DAT data and well fluid contacts
2) Net to Gross obtained from well logs
3) Porosity obtained from well logs
4) Oil/Gas saturation obtained from well logs and
5) FVF obtained from PVT analyses
To account for uncertainties in estimating the reserve, three cases were considered;
1) Minimum case (P10)- lowest OIIP
2) Average case(P50)- average OIIP
3) Maximum case (P90)- highest OIIP
The following conclusions were drawn during and after estimating HCIIP;
1) Alwyn North field Brent East is an oil field with no gas cap.
2) The geological structures showed the presence of two faults and some tilted folds.
3) The WOC was consistent at about 3,231m showing that the reservoir is continuous and
connected and there is a high likelihood that the faults are non-sealing.
4) Tarbert 3 has the highest reservoir thickness with the best reservoir petrophysical
characteristics (permeability, oil saturation and porosity) making it the most contributor
to the estimated reserve. Tarbert 2 has a lot of mica embedded in its sandstones. All the
wells had about the same WOC Ness 1 was in the aquifer zone and could not be produced
from.
5) The OIIP of the field was found to be :
Minimum case = 19,253,824.44 m3
Average case = 31,421,555.11 m3
Maximum case = 39,837,677.39 m3
47. Page | 37
T3 is the largest contributor to the OIIP in the field due to its high porosity, high reservoir
thickness and low water saturations.
48. Page | 38
REFERENCES
1) http://www.spe.org/index.php
2) Owil N (2018): Lecture Note: Hydrocarbon‐ In‐ Place Estimation. March 5‐ 9, 2018 –
IPS
49. Page | 39
APPENDIX
Appendix I: Resistivity and Saturation of Formation Water in the Aquifer (N1)
In the water zone Sw= 1 (obtained from logs), Rt = 4Ωm, up.16 , m =2, n =1,
a =0.81
Assuming the sandstones is clean,
From equation (8), mw
w
a
R
R
2
16.0
81.0
4
x 126.0
Appendix II: Calculation of FVF
𝐵𝑜𝑐 =
𝑉(𝑃)
𝑉(𝑃𝑠𝑎𝑡)
× 𝐵𝑜𝑝( 𝑃𝑠𝑎𝑡) (16)
𝐵𝑜𝑝 =
𝑉𝑜
𝑉𝑠𝑐
=
𝑉(𝑃)
𝑉𝑠𝑎𝑡
×
𝑉𝑠𝑎𝑡
𝑉𝑠𝑐
(19)
For well A4 @ reservoir conditions (112.1℃ and 445.4 bar(g))
𝐵𝑜𝑝( 𝑃𝑠𝑎𝑡) = 1.711
𝑉𝑜
𝑉𝑠𝑎𝑡
= 0.9571
𝐵𝑜𝑐 = 0.9571 × 1.711
= 1.6376
For well N3 @ reservoir conditions (111℃ and 445 bar(g))
𝐵𝑜𝑝( 𝑃𝑠𝑎𝑡) = 1.664
𝑉𝑜
𝑉𝑠𝑎𝑡
= 0.955
𝐵𝑜𝑐 = 0.955 × 1.664
= 1.589
Appendix III: Data Sheets Obtained During Calculation
1. Excel Spreadsheet (HCIIP Estimation)
2. Well-to-Well Correlation
50. Page | 40
Table 14: Well to Well Correlation Data Sheet a) North- South b) West- East
(a)
(b)
3. Depth-Area Data
Table 15: Depth-Area Data Sheet a) Non-eroded b) Eroded
51. Page | 41
a)
b)
4. Petrophysical Properties Data Sheet
Table 16: Petrophysical Properties Data Sheet