One of the challenges of the petroleum industry is achieving maximum recovery from oil reservoirs. The natural energy of the reservoir, primary recoveries in most cases do not exceed 20%. To improve recovery, secondary recovery techniques are employed. With secondary recovery techniques such as waterflooding, an incremental recovery ranging from 15 to 25% can be achieved. Several theories and methods have been developed for predicting waterflood performance. The Dykstra-Parson technique stands as the most widely used of these methods. The authors developed a discrete, analytical solution from which the vertical coverage, water-oil ratio, cumulative oil produced, cumulative water produced and injected, and the time required for injection was determined. Reznik et al extended the work of Dykstra and Parson to include exact, analytical, continuous solutions, with explicit solutions for time, constant injection pressure, and constant overall injection rate conditions, property time, real or process time, with the assumption of piston-like displacement. This work presents a computer implementation to compare the results of the Dykstra and Parson method, and the Reznik et al extension. A user-friendly graphical user interface executable application has been developed for both methods using Python 3. The application provides an interactive GUI output for graphs and tables with the python matplotlib module, and Pandastable. The GUI was built with Tkinter and converted to an executable desktop application using Pyinstaller and the Nullsoft Scriptable Install System, to serve as a hands-on tool for petroleum engineers and the industry. The results of the program for both methods gave a close match with that obtained from the simulation performed with Flow (Open Porous Media). The results provided more insight into the underlying principles and applications of the methods.
{"title":"Computer Implementation of the Dykstra-Parsons Method of Waterflood Calculation","authors":"P. Lekia","doi":"10.2118/207151-ms","DOIUrl":"https://doi.org/10.2118/207151-ms","url":null,"abstract":"\u0000 One of the challenges of the petroleum industry is achieving maximum recovery from oil reservoirs. The natural energy of the reservoir, primary recoveries in most cases do not exceed 20%. To improve recovery, secondary recovery techniques are employed. With secondary recovery techniques such as waterflooding, an incremental recovery ranging from 15 to 25% can be achieved. Several theories and methods have been developed for predicting waterflood performance.\u0000 The Dykstra-Parson technique stands as the most widely used of these methods. The authors developed a discrete, analytical solution from which the vertical coverage, water-oil ratio, cumulative oil produced, cumulative water produced and injected, and the time required for injection was determined. Reznik et al extended the work of Dykstra and Parson to include exact, analytical, continuous solutions, with explicit solutions for time, constant injection pressure, and constant overall injection rate conditions, property time, real or process time, with the assumption of piston-like displacement.\u0000 This work presents a computer implementation to compare the results of the Dykstra and Parson method, and the Reznik et al extension. A user-friendly graphical user interface executable application has been developed for both methods using Python 3. The application provides an interactive GUI output for graphs and tables with the python matplotlib module, and Pandastable. The GUI was built with Tkinter and converted to an executable desktop application using Pyinstaller and the Nullsoft Scriptable Install System, to serve as a hands-on tool for petroleum engineers and the industry.\u0000 The results of the program for both methods gave a close match with that obtained from the simulation performed with Flow (Open Porous Media). The results provided more insight into the underlying principles and applications of the methods.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84891212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a model for assessing Subsea Control Module (SCM) integrity for re-use after decommissioning. The SCM is one of the most failure-prone components in subsea oil and gas developments, and although a relatively inexpensive component in a Subsea Production System (SPS), the failure of an SCM can require production to be ceased, significantly impacting project economics. The re-use of decommissioned SCMs could improve the economics of subsea oil and gas projects by reducing design and manufacturing lead times, improving production availability, and enabling the economic exploitation of both marginal and mature fields. Insights gained through relevant literature, industry standards, and subsea industry experts led to the development of the Integrity Assessment Model for Decommissioned Subsea Control Modules proposed in this paper. The model is based on an Integrity Assessment process as well as a Risk-Based Compatibility Assessment. Discussions include a background of the SCM and common failures, the economics of SCM re-use, and Applications for the re-use of SCMs. The Integrity Assessment Model is described in detail, as well as the benefits and limitations. Areas of future research in support of subsea equipment re-use are identified, including improvements to reliability databases for subsea equipment, and the creation of a quantitative metric describing subsea equipment integrity.
{"title":"A Suitable Model for Assessing the Integrity of Subsea Control Modules SCMs for Re-Use after Decommissioning","authors":"Erica Root, J. Andrawus, I. Iyalla","doi":"10.2118/207205-ms","DOIUrl":"https://doi.org/10.2118/207205-ms","url":null,"abstract":"\u0000 This paper proposes a model for assessing Subsea Control Module (SCM) integrity for re-use after decommissioning. The SCM is one of the most failure-prone components in subsea oil and gas developments, and although a relatively inexpensive component in a Subsea Production System (SPS), the failure of an SCM can require production to be ceased, significantly impacting project economics. The re-use of decommissioned SCMs could improve the economics of subsea oil and gas projects by reducing design and manufacturing lead times, improving production availability, and enabling the economic exploitation of both marginal and mature fields. Insights gained through relevant literature, industry standards, and subsea industry experts led to the development of the Integrity Assessment Model for Decommissioned Subsea Control Modules proposed in this paper. The model is based on an Integrity Assessment process as well as a Risk-Based Compatibility Assessment. Discussions include a background of the SCM and common failures, the economics of SCM re-use, and Applications for the re-use of SCMs. The Integrity Assessment Model is described in detail, as well as the benefits and limitations. Areas of future research in support of subsea equipment re-use are identified, including improvements to reliability databases for subsea equipment, and the creation of a quantitative metric describing subsea equipment integrity.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"329 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79722492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pressure transient analysis has been used to evaluate performance of a vertical well located within two intersecting sealing faults. The nature and types of boundary affect productivity in bounded reservoirs. Well performance is strongly affected by well location with respect to the boundary, be it single, paired and parallel or paired and inclined. The goal of this research was to study pressure behavior as well as performance of a vertical well located within two intersecting sealing faults inclined at various angles θ and at unequal distances to faults. Unlike similar works previously carried out, this work can be used to study or predict pressure distribution of a well in a wedge system located at unequal distances to faults. Using the concept of images, the study proposed new models for estimating distances between image well(s) and active well. These models were applied in the solution to the dimensionless diffusivity equation to characterize pressure transient behavior of a well located at unequal distances to the inclined faults. These pressures and pressure derivatives were computed from the total pressure drop expression summing all the image wells by the principle of superposition. The MATLAB, Python and Excel software were deployed to compute all the dimensionless pressures for the different well designs. The results obtained show that 1) the proposed models give accurate estimation of active well distances to image wells; 2) the models show that the distance between the active and image wells d0,i increases for the range of values of angles 0°< θ0,i ≤ 180° and decreases for the range 180° < θ0,i < 360°; 3) the relationship between unequal well distances and productivity has a maximum point; 4) beyond this point, the well ceases to be productive and; 5) this maximum point is at equal distances of the well from both faults, in this case, 15 ft. Larger magnitudes of dimensionless pressure derivatives would indicate higher oil production for any well design and inclination of the boundaries. Worthy of future works are similar studies on 1) horizontal wells and 2) mixed boundaries, that is, one sealing fault and one constant pressure boundary.
{"title":"Pressures and Pressure Derivatives of a Vertical Well Located Within Two Inclined Faults: Case Study of Basic Angles and Unequal Well Distances from Faults","authors":"M. Ojah, S. Adewole","doi":"10.2118/207138-ms","DOIUrl":"https://doi.org/10.2118/207138-ms","url":null,"abstract":"\u0000 Pressure transient analysis has been used to evaluate performance of a vertical well located within two intersecting sealing faults. The nature and types of boundary affect productivity in bounded reservoirs. Well performance is strongly affected by well location with respect to the boundary, be it single, paired and parallel or paired and inclined. The goal of this research was to study pressure behavior as well as performance of a vertical well located within two intersecting sealing faults inclined at various angles θ and at unequal distances to faults. Unlike similar works previously carried out, this work can be used to study or predict pressure distribution of a well in a wedge system located at unequal distances to faults. Using the concept of images, the study proposed new models for estimating distances between image well(s) and active well. These models were applied in the solution to the dimensionless diffusivity equation to characterize pressure transient behavior of a well located at unequal distances to the inclined faults. These pressures and pressure derivatives were computed from the total pressure drop expression summing all the image wells by the principle of superposition. The MATLAB, Python and Excel software were deployed to compute all the dimensionless pressures for the different well designs. The results obtained show that 1) the proposed models give accurate estimation of active well distances to image wells; 2) the models show that the distance between the active and image wells d0,i increases for the range of values of angles 0°< θ0,i ≤ 180° and decreases for the range 180° < θ0,i < 360°; 3) the relationship between unequal well distances and productivity has a maximum point; 4) beyond this point, the well ceases to be productive and; 5) this maximum point is at equal distances of the well from both faults, in this case, 15 ft. Larger magnitudes of dimensionless pressure derivatives would indicate higher oil production for any well design and inclination of the boundaries. Worthy of future works are similar studies on 1) horizontal wells and 2) mixed boundaries, that is, one sealing fault and one constant pressure boundary.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77806696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Production ramp-up can be a tricky exercise, with a repertoire of challenges and surprises, which can threaten the proposed percentage of success. These challenges largely stem from paucity of data, change in reservoir dynamics, impact of unauthorized third-party activities and the compliment of skill sets/experience in the team. In addition, reservoir models are in some cases, ineffective tools for reservoir management and prediction. This is especially true in assets that are heavily bunkered by crude oil thieves and in geologically complex reservoirs with very few wells. Therefore, models must be updated with recent data and new understanding, to remain useful and accurate. Of great importance to a successful production ramp-up effort is assembling an integrated team of experienced sub-surface professionals and field executors. Unfortunately, in some teams and companies, the position and opinions of Geologists are often overlooked, resulting in mixed outcomes. Geologists do not only think in "millions of years" but can add value and contribute significantly to the overall project objective. This paper explores and explains the role of a geologist, as part of an integrated asset management team, in the successful ramp-up of production from 45KOPD to about 80KOPD, without drilling a single well or executing any rig activity. It also showcases some work methodologies adopted in the evaluation of candidate wells and the power inherent in adopting an integrated subsurface approach.
{"title":"Project 80KOPD: A Geologist's Perspective to a Successful Production Ramp-Up Effort.","authors":"Valentine Ihebuzor","doi":"10.2118/207123-ms","DOIUrl":"https://doi.org/10.2118/207123-ms","url":null,"abstract":"\u0000 Production ramp-up can be a tricky exercise, with a repertoire of challenges and surprises, which can threaten the proposed percentage of success. These challenges largely stem from paucity of data, change in reservoir dynamics, impact of unauthorized third-party activities and the compliment of skill sets/experience in the team. In addition, reservoir models are in some cases, ineffective tools for reservoir management and prediction. This is especially true in assets that are heavily bunkered by crude oil thieves and in geologically complex reservoirs with very few wells. Therefore, models must be updated with recent data and new understanding, to remain useful and accurate. Of great importance to a successful production ramp-up effort is assembling an integrated team of experienced sub-surface professionals and field executors. Unfortunately, in some teams and companies, the position and opinions of Geologists are often overlooked, resulting in mixed outcomes. Geologists do not only think in \"millions of years\" but can add value and contribute significantly to the overall project objective. This paper explores and explains the role of a geologist, as part of an integrated asset management team, in the successful ramp-up of production from 45KOPD to about 80KOPD, without drilling a single well or executing any rig activity. It also showcases some work methodologies adopted in the evaluation of candidate wells and the power inherent in adopting an integrated subsurface approach.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81519465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During multiphase flow, there is a variation of the physical distribution of the phases within the conduit leading to different flow regimes and consequently variation in the pressure gradient along with the flow regime, hence flow parameter is of vital importance in the prediction of flow regime and pressure gradient in multiphase flow. Analytical solutions and empirical correlations have been developed to predict the flow regime and the pressure gradient respectively. However, in this study, we seek to use supervised machine learning to make predictions taking parameters such as relative phase volume, bulk fluid flow rates, individual phase flow rates, conduit diameters, inclination, phase densities and temperature as input to the model. The data representing these parameters can be regularly updated to reflect the flow conditions in the well. The flow is composed of water, oil and air at different temperatures. The machine learning models used are Logistic Regression, Decision Trees and Principal Component Analysis. The first two is supervised and are tuned for accuracy dependent on pressure gauge readings while the third seeks to determine the parameters of greatest influence on the predicted output, the flow regime and pressure gradient. The model is constrained to learning and making predictions for fluid production through the tubing only. The trained model shows promise for application in the industry as it allows for automation of systems used to control flow and affords a more comprehensive approach to mitigating flow problems in pipeline systems and flow systems in oilfields.
{"title":"Investigating the Use of Machine Learning Models for the Prediction of Pressure Gradient and Flow Regimes in Multiphase Flow in Horizontal Pipes","authors":"Isemin. A. Isemin, King-Akanimo B. Nkundu","doi":"10.2118/208410-ms","DOIUrl":"https://doi.org/10.2118/208410-ms","url":null,"abstract":"\u0000 During multiphase flow, there is a variation of the physical distribution of the phases within the conduit leading to different flow regimes and consequently variation in the pressure gradient along with the flow regime, hence flow parameter is of vital importance in the prediction of flow regime and pressure gradient in multiphase flow. Analytical solutions and empirical correlations have been developed to predict the flow regime and the pressure gradient respectively. However, in this study, we seek to use supervised machine learning to make predictions taking parameters such as relative phase volume, bulk fluid flow rates, individual phase flow rates, conduit diameters, inclination, phase densities and temperature as input to the model. The data representing these parameters can be regularly updated to reflect the flow conditions in the well. The flow is composed of water, oil and air at different temperatures. The machine learning models used are Logistic Regression, Decision Trees and Principal Component Analysis. The first two is supervised and are tuned for accuracy dependent on pressure gauge readings while the third seeks to determine the parameters of greatest influence on the predicted output, the flow regime and pressure gradient.\u0000 The model is constrained to learning and making predictions for fluid production through the tubing only. The trained model shows promise for application in the industry as it allows for automation of systems used to control flow and affords a more comprehensive approach to mitigating flow problems in pipeline systems and flow systems in oilfields.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78827924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There are numerous problems encountered during drilling such as wellbore instability, drilling mud weight estimation, as well as selecting good casing and bit for the drilling operations. It is therefore important to understand and accurately determine the strength of the rock in order to avoid these common drilling problems which are mostly encountered during well operations. It is of paramount importance to determine uniaxial compressive strength (UCS) from core and sonic log data so as to accurately predict rock strength for better well planning. In this work, we were able to obtain a correlation to determine UCS from data obtained from ten (10) wells in different locations in onshore Niger Delta using the regression analysis method. The correlation of UCS versus Poisson's ratio gave R2 value of 90.0%. The R2- value tending towards one (1) indicates that this model can be reliably used to predict ND-UCS and the p<0.05 shows that there is significant relationship between ND-UCS and Poisson's ratio. The model was validated with an entirely different well data and it predicted over 89% rock UCS data when compared to the actual rock UCS data. This study also provides an understanding of the variation in UCS and Poisson's ratio with depth for effective rock property analysis and evaluation. These correlations will help well engineers to make informed decisions on rock strength predictions during well planning and operations as well as manage wellbore stability optimally.
{"title":"Efficient Wellbore Optimization Through Rock Property Analysis and Evaluation","authors":"Joshua Oluwayomi Ogunrinde","doi":"10.2118/207111-ms","DOIUrl":"https://doi.org/10.2118/207111-ms","url":null,"abstract":"\u0000 There are numerous problems encountered during drilling such as wellbore instability, drilling mud weight estimation, as well as selecting good casing and bit for the drilling operations. It is therefore important to understand and accurately determine the strength of the rock in order to avoid these common drilling problems which are mostly encountered during well operations. It is of paramount importance to determine uniaxial compressive strength (UCS) from core and sonic log data so as to accurately predict rock strength for better well planning. In this work, we were able to obtain a correlation to determine UCS from data obtained from ten (10) wells in different locations in onshore Niger Delta using the regression analysis method. The correlation of UCS versus Poisson's ratio gave R2 value of 90.0%. The R2- value tending towards one (1) indicates that this model can be reliably used to predict ND-UCS and the p<0.05 shows that there is significant relationship between ND-UCS and Poisson's ratio. The model was validated with an entirely different well data and it predicted over 89% rock UCS data when compared to the actual rock UCS data. This study also provides an understanding of the variation in UCS and Poisson's ratio with depth for effective rock property analysis and evaluation. These correlations will help well engineers to make informed decisions on rock strength predictions during well planning and operations as well as manage wellbore stability optimally.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"101 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77270841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Nwankwo, Jennifer. S Muku, Oladipo G. Ogunbona, Chidi. B Ike, M. Amosa, Ebipador Ogionwo
The Offshore Safety Permit (OSP) Program is the Personnel Accountability System, being utilized by the Nigerian Oil and Gas Industry Regulator, in line with global best practices to manage the details of over 40,000 oil workers registered to work on offshore and swamp facilities and track their movements to-and-fro such facilities. The Program was introduced in 2012 to standardize requirement for personnel travel to offshore and swamp locations and to eliminate issues such as: non-compliance with mandatory competency and safety training; non-compliance with medical fitness to work requirement; unauthorized extended stay on facilities at offshore/remote location; inaccurate documentation of personnel movement to-and-fro facilities at offshore/remote location leading to delayed/wrong incident reporting. This paper examines, through the review of the OSP policy, Guidelines and database, the value addition of the program since its inception., detailed and insightful discussions are made on the importance and potentials of the OSP program as a simple but integral policy and planning tool in managing risks, enhancing collaboration and improving safety and emergency services in Nigeria's oil and gas industry.
{"title":"The Implementation of Offshore Safety Program OSP in Nigerian Oil and Gas Industry- A Performance Assessment","authors":"O. Nwankwo, Jennifer. S Muku, Oladipo G. Ogunbona, Chidi. B Ike, M. Amosa, Ebipador Ogionwo","doi":"10.2118/207121-ms","DOIUrl":"https://doi.org/10.2118/207121-ms","url":null,"abstract":"\u0000 The Offshore Safety Permit (OSP) Program is the Personnel Accountability System, being utilized by the Nigerian Oil and Gas Industry Regulator, in line with global best practices to manage the details of over 40,000 oil workers registered to work on offshore and swamp facilities and track their movements to-and-fro such facilities. The Program was introduced in 2012 to standardize requirement for personnel travel to offshore and swamp locations and to eliminate issues such as: non-compliance with mandatory competency and safety training; non-compliance with medical fitness to work requirement; unauthorized extended stay on facilities at offshore/remote location; inaccurate documentation of personnel movement to-and-fro facilities at offshore/remote location leading to delayed/wrong incident reporting. This paper examines, through the review of the OSP policy, Guidelines and database, the value addition of the program since its inception., detailed and insightful discussions are made on the importance and potentials of the OSP program as a simple but integral policy and planning tool in managing risks, enhancing collaboration and improving safety and emergency services in Nigeria's oil and gas industry.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89105016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Valentine Ihebuzor, O. Onyeneke, A. Adebari, Obasi Ogbonnaya
Reserves are typically estimated and re-validated throughout the life of a producing field. The accuracy of this estimation is based on the availability of relevant and current data from that reservoir or field and other factors. There are several methods for estimating reserves, but the choice of which method is to be applied is often based on the data available per time. However, these methods are known to be associated with varying degrees of uncertainties arising from quality of data, the assumptions adopted and the experience of the evaluator. The biggest uncertainty in reserve estimation lies in the inability of the commonly available methods to estimate and discount the huge volumes lost due to unauthorized production by third parties, through crude oil theft, illegal bunkering activities, and spills. This leads to the gross overestimation of reserves and the economic viability of an asset, especially in onshore and shallow offshore assets where such illegal activities are typical and rampant. This paper showcases an approach of estimating reserves, through the integration of multidisciplinary data, which enables the estimation and discounting of crude oil volumes lost due to illegal production from a reservoir.
{"title":"An Approach to Estimating Reserve Volumes by Discounting Unauthorized Production Via Crude Oil Theft","authors":"Valentine Ihebuzor, O. Onyeneke, A. Adebari, Obasi Ogbonnaya","doi":"10.2118/207110-ms","DOIUrl":"https://doi.org/10.2118/207110-ms","url":null,"abstract":"\u0000 Reserves are typically estimated and re-validated throughout the life of a producing field. The accuracy of this estimation is based on the availability of relevant and current data from that reservoir or field and other factors. There are several methods for estimating reserves, but the choice of which method is to be applied is often based on the data available per time. However, these methods are known to be associated with varying degrees of uncertainties arising from quality of data, the assumptions adopted and the experience of the evaluator. The biggest uncertainty in reserve estimation lies in the inability of the commonly available methods to estimate and discount the huge volumes lost due to unauthorized production by third parties, through crude oil theft, illegal bunkering activities, and spills. This leads to the gross overestimation of reserves and the economic viability of an asset, especially in onshore and shallow offshore assets where such illegal activities are typical and rampant. This paper showcases an approach of estimating reserves, through the integration of multidisciplinary data, which enables the estimation and discounting of crude oil volumes lost due to illegal production from a reservoir.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89414696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Ijomanta, Lukman Lawal, Onyeka Ike, Raymond Olugbade, Fanen Gbuku, Charles Akenobo
This paper presents an overview of the implementation of a Digital Oilfield (DOF) system for the real-time management of the Oredo field in OML 111. The Oredo field is predominantly a retrograde condensate field with a few relatively small oil reservoirs. The field operating philosophy involves the dual objective of maximizing condensate production and meeting the daily contractual gas quantities which requires wells to be controlled and routed such that the dual objectives are met. An Integrated Asset Model (IAM) (or an Integrated Production System Model) was built with the objective of providing a mathematical basis for meeting the field's objective. The IAM, combined with a Model Management and version control tool, a workflow orchestration and automation engine, A robust data-management module, an advanced visualization and collaboration environment and an analytics library and engine created the Oredo Digital Oil Field (DOF). The Digital Oilfield is a real-time digital representation of a field on a computer which replicates the behavior of the field. This virtual field gives the engineer all the information required to make quick, sound and rational field management decisions with models, workflows, and intelligently filtered data within a multi-disciplinary organization of diverse capabilities and engineering skill sets. The creation of the DOF involved 4 major steps; DATA GATHERING considered as the most critical in such engineering projects as it helps to set the limits of what the model can achieve and cut expectations. ENGINEERING MODEL REVIEW, UPDATE AND BENCHMARKING; Majorly involved engineering models review and update, real-time data historian deployment etc. SYSTEM PRECONFIGURATION AND DEPLOYMENT; Developed the DOF system architecture and the engineering workflow setup. POST DEPLOYMENT REVIEW AND UPDATE; Currently ongoing till date, this involves after action reviews, updates and resolution of challenges of the DOF, capability development by the operator and optimizing the system for improved performance. The DOF system in the Oredo field has made it possible to integrate, automate and streamline the execution of field management tasks and has significantly reduced the decision-making turnaround time. Operational and field management decisions can now be made within minutes rather than weeks or months. The gains and benefits cuts across the entire production value chain from improved operational safety to operational efficiency and cost savings, real-time production surveillance, optimized production, early problem detection, improved Safety, Organizational/Cross-discipline collaboration, data Centralization and Efficiency. The DOF system did not come without its peculiar challenges observed both at the planning, execution and post evaluation stages which includes selection of an appropriate Data Gathering & acquisition system, Parts interchangeability and device integration with existing field devices, high data latency
{"title":"Digital Oil Field; The NPDC Experience","authors":"H. Ijomanta, Lukman Lawal, Onyeka Ike, Raymond Olugbade, Fanen Gbuku, Charles Akenobo","doi":"10.2118/207169-ms","DOIUrl":"https://doi.org/10.2118/207169-ms","url":null,"abstract":"\u0000 This paper presents an overview of the implementation of a Digital Oilfield (DOF) system for the real-time management of the Oredo field in OML 111.\u0000 The Oredo field is predominantly a retrograde condensate field with a few relatively small oil reservoirs. The field operating philosophy involves the dual objective of maximizing condensate production and meeting the daily contractual gas quantities which requires wells to be controlled and routed such that the dual objectives are met.\u0000 An Integrated Asset Model (IAM) (or an Integrated Production System Model) was built with the objective of providing a mathematical basis for meeting the field's objective. The IAM, combined with a Model Management and version control tool, a workflow orchestration and automation engine, A robust data-management module, an advanced visualization and collaboration environment and an analytics library and engine created the Oredo Digital Oil Field (DOF).\u0000 The Digital Oilfield is a real-time digital representation of a field on a computer which replicates the behavior of the field. This virtual field gives the engineer all the information required to make quick, sound and rational field management decisions with models, workflows, and intelligently filtered data within a multi-disciplinary organization of diverse capabilities and engineering skill sets.\u0000 The creation of the DOF involved 4 major steps;\u0000 DATA GATHERING considered as the most critical in such engineering projects as it helps to set the limits of what the model can achieve and cut expectations. ENGINEERING MODEL REVIEW, UPDATE AND BENCHMARKING; Majorly involved engineering models review and update, real-time data historian deployment etc. SYSTEM PRECONFIGURATION AND DEPLOYMENT; Developed the DOF system architecture and the engineering workflow setup. POST DEPLOYMENT REVIEW AND UPDATE; Currently ongoing till date, this involves after action reviews, updates and resolution of challenges of the DOF, capability development by the operator and optimizing the system for improved performance.\u0000 The DOF system in the Oredo field has made it possible to integrate, automate and streamline the execution of field management tasks and has significantly reduced the decision-making turnaround time. Operational and field management decisions can now be made within minutes rather than weeks or months. The gains and benefits cuts across the entire production value chain from improved operational safety to operational efficiency and cost savings, real-time production surveillance, optimized production, early problem detection, improved Safety, Organizational/Cross-discipline collaboration, data Centralization and Efficiency.\u0000 The DOF system did not come without its peculiar challenges observed both at the planning, execution and post evaluation stages which includes selection of an appropriate Data Gathering & acquisition system,\u0000 Parts interchangeability and device integration with existing field devices, high data latency","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89419470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Carbon steel pipelines are used to transport hydrocarbons globally because carbon steel is relatively easier to fabricate, safe for use, raw materials are available and less expensive. Amidst these benefits, carbon steel is susceptible to severe corrosion and other anomalies. Pipeline corrosion is a significant concern in the oil and gas industry. It has caused several minor and catastrophic losses of containment with resultant fatalities, environmental pollutions, asset damage, and production downtimes. The increasing failures of in-service pipelines have led the Department of Petroleum Resources (DPR) to intensify regulatory scrutiny of pipeline integrity assessment and management in Nigeria to ensure strict compliance to the regulatory requirements by the Oil Producing Companies. According to DPR Act (Section 2.5.2.1), all pipelines greater than 6" size diameter must be inspected every five (5) years with intelligent pigs (inline inspection tools) that would provide the accurate condition of the pipeline. However, many pipelines in Nigeria are unpiggable or difficult to inspect with intelligent pigs due to the unavailability of pigging facilities (especially in brownfields), pipelines with short bend radiuses, dual diameters, flow parameters, etcetera. This paper explores case studies involving the use of advanced inline inspection technology to conduct inline inspection of difficult-to-inspect dual-diameter pipelines.
{"title":"Overcoming Challenges of Pigging the Unpiggable Pipelines","authors":"Chinedu Oragwu, D. Molyneux, L. Lawal, S. Ameh","doi":"10.2118/207147-ms","DOIUrl":"https://doi.org/10.2118/207147-ms","url":null,"abstract":"\u0000 Carbon steel pipelines are used to transport hydrocarbons globally because carbon steel is relatively easier to fabricate, safe for use, raw materials are available and less expensive. Amidst these benefits, carbon steel is susceptible to severe corrosion and other anomalies. Pipeline corrosion is a significant concern in the oil and gas industry. It has caused several minor and catastrophic losses of containment with resultant fatalities, environmental pollutions, asset damage, and production downtimes.\u0000 The increasing failures of in-service pipelines have led the Department of Petroleum Resources (DPR) to intensify regulatory scrutiny of pipeline integrity assessment and management in Nigeria to ensure strict compliance to the regulatory requirements by the Oil Producing Companies. According to DPR Act (Section 2.5.2.1), all pipelines greater than 6\" size diameter must be inspected every five (5) years with intelligent pigs (inline inspection tools) that would provide the accurate condition of the pipeline. However, many pipelines in Nigeria are unpiggable or difficult to inspect with intelligent pigs due to the unavailability of pigging facilities (especially in brownfields), pipelines with short bend radiuses, dual diameters, flow parameters, etcetera.\u0000 This paper explores case studies involving the use of advanced inline inspection technology to conduct inline inspection of difficult-to-inspect dual-diameter pipelines.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86340824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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