H. R. Sutoyo, Diniko Nurhajj, Anak Agung Gde Iswara Anindyajati, Dwi Hudya Febrianto, Nova Kristianawatie
Early production of gas reservoirs is usually associated with a volumetric gas driving mechanism with no water production. Aquifer activity is minimal as well during the early life of the reservoir. In this paper, we will discuss about the good engineering practices based on several shut-in pressure data to observe and maximize marginal gas field value. We will also discuss about the possibility of water drive behavior in this field. Shut-in pressure data plays an important role in determining the in-place and reservoir dynamics of the gas reservoir. High shut-in pressure usually indicates high gas reserves. On the other hand, it shows a very strong water drive existence. The study takes place on a sandstone gas reservoir with an abnormal pressure regime on it. Production performance was then analyzed using the rate transient analysis (RTA) to determine its properties and gas in place and crosschecked with shut-in pressure data. From these steps, we can determine the trend of both static and flowing material balance (FMB) analysis to predict the reservoir dynamics. During the early life of production, it is clear that volumetric reservoir plays an important role in the reservoir dynamics since it produces no reservoir water. However, after 1 year of production, it starts to produce reservoir water. Monitoring starts when the first shut-in pressure shows a quite unexpected value. It puts a sense of both high gas reserves and aquifer activity. After applying all the pressure and production data on FMB and p/Z plot, it shows that both high gas reserves and aquifer activity exist in this field. The results of this study change the development strategy of this field, preventing doing major investment on high capital expenditure (CAPEX) with low results due to high aquifer activity. We can conclude that good reservoir monitoring and analysis combining several analytical methods can enhance our insight into reservoir dynamics. Combining FMB and p/Z, geologist starts to compare aquifer volume based on geological data and found to be similar with the results coming from analytical data. 3D reservoir simulation also confirms similar results based on those analyses.
{"title":"High Shut-in Pressure: Good News or Bad News? Maximising Value through Limited Data","authors":"H. R. Sutoyo, Diniko Nurhajj, Anak Agung Gde Iswara Anindyajati, Dwi Hudya Febrianto, Nova Kristianawatie","doi":"10.2118/205796-ms","DOIUrl":"https://doi.org/10.2118/205796-ms","url":null,"abstract":"\u0000 Early production of gas reservoirs is usually associated with a volumetric gas driving mechanism with no water production. Aquifer activity is minimal as well during the early life of the reservoir. In this paper, we will discuss about the good engineering practices based on several shut-in pressure data to observe and maximize marginal gas field value. We will also discuss about the possibility of water drive behavior in this field.\u0000 Shut-in pressure data plays an important role in determining the in-place and reservoir dynamics of the gas reservoir. High shut-in pressure usually indicates high gas reserves. On the other hand, it shows a very strong water drive existence. The study takes place on a sandstone gas reservoir with an abnormal pressure regime on it. Production performance was then analyzed using the rate transient analysis (RTA) to determine its properties and gas in place and crosschecked with shut-in pressure data. From these steps, we can determine the trend of both static and flowing material balance (FMB) analysis to predict the reservoir dynamics.\u0000 During the early life of production, it is clear that volumetric reservoir plays an important role in the reservoir dynamics since it produces no reservoir water. However, after 1 year of production, it starts to produce reservoir water. Monitoring starts when the first shut-in pressure shows a quite unexpected value. It puts a sense of both high gas reserves and aquifer activity. After applying all the pressure and production data on FMB and p/Z plot, it shows that both high gas reserves and aquifer activity exist in this field. The results of this study change the development strategy of this field, preventing doing major investment on high capital expenditure (CAPEX) with low results due to high aquifer activity. We can conclude that good reservoir monitoring and analysis combining several analytical methods can enhance our insight into reservoir dynamics.\u0000 Combining FMB and p/Z, geologist starts to compare aquifer volume based on geological data and found to be similar with the results coming from analytical data. 3D reservoir simulation also confirms similar results based on those analyses.","PeriodicalId":11052,"journal":{"name":"Day 3 Thu, October 14, 2021","volume":"96 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73210442","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}
Edwin Lawrence, Marie Bjoerdal Loevereide, Sanggeetha Kalidas, Ngoc Le Le, Sarjono Tasi Antoneus, Tu Le Mai Khanh
As part of the production optimization exercise in J field, an initiative has been taken to enhance the field production target without well intervention. J field is a mature field; the wells are mostly gas lifted, and currently it is in production decline mode. As part of this optimization exercise, a network model with multiple platforms was updated with the surface systems (separator, compressors, pumps, FPSO) and pipelines in place to understand the actual pressure drop across the system. Modelling and calibration of the well and network model was done for the entire field, and the calibrated model was used for the production optimization exercise. A representative model updated with the current operating conditions is the key for the field production and asset management. In this exercise, a multiphase flow simulator for wells and pipelines has been utilized. A total of ∼50 wells (inclusive of idle wells) has been included in the network model. Basically, the exercise started by updating the single-well model using latest well test data. During the calibration at well level, several steps were taken, such as evaluation of historical production, reservoir pressure, and well intervention. This will provide a better idea on the fine-tuning parameters. Upon completion of calibrating well models, the next level was calibration of network model at the platform level by matching against the platform operating conditions (platform production rates, separator/pipeline pressure). The last stage was performing field network model calibration to match the overall field performance. During the platform stage calibration, some parameters such as pipeline ID, horizontal flow correlation, friction factor, and holdup factor were fine-tuned to match the platform level operating conditions. Most of the wells in J field have been calibrated by meeting the success criterion, which is within +/-5% for the production rates. However, there were some challenges in matching several wells due to well test data validity especially wells located on remote platform where there is no dedicated test separator as well as the impact of gas breakthrough, which may interfere to performance of wells. These wells were decided to be retested in the following month. As for the platform level matching, five platforms were matched within +/-10% against the reported production rates. During the evaluation, it was observed there were some uncertainties in the reported water and gas rates (platform level vs. well test data). This is something that can be looked into for a better measurement in the future. By this observation, it was suggested to select Platform 1 with the most reliable test data as well as the platform rate for the optimization process and qualifying for the field trial. Nevertheless, with the representative network model, two scenarios, reducing separator pressure at platform level and gas lift optimization by an optimal gas lift rate allocation, were performed. T
{"title":"Production Optimization in Mature Field Through Scenario Prediction Using a Representative Network Model: A Rapid Solution Without Well Intervention","authors":"Edwin Lawrence, Marie Bjoerdal Loevereide, Sanggeetha Kalidas, Ngoc Le Le, Sarjono Tasi Antoneus, Tu Le Mai Khanh","doi":"10.2118/205662-ms","DOIUrl":"https://doi.org/10.2118/205662-ms","url":null,"abstract":"\u0000 As part of the production optimization exercise in J field, an initiative has been taken to enhance the field production target without well intervention. J field is a mature field; the wells are mostly gas lifted, and currently it is in production decline mode. As part of this optimization exercise, a network model with multiple platforms was updated with the surface systems (separator, compressors, pumps, FPSO) and pipelines in place to understand the actual pressure drop across the system. Modelling and calibration of the well and network model was done for the entire field, and the calibrated model was used for the production optimization exercise.\u0000 A representative model updated with the current operating conditions is the key for the field production and asset management. In this exercise, a multiphase flow simulator for wells and pipelines has been utilized. A total of ∼50 wells (inclusive of idle wells) has been included in the network model. Basically, the exercise started by updating the single-well model using latest well test data. During the calibration at well level, several steps were taken, such as evaluation of historical production, reservoir pressure, and well intervention. This will provide a better idea on the fine-tuning parameters. Upon completion of calibrating well models, the next level was calibration of network model at the platform level by matching against the platform operating conditions (platform production rates, separator/pipeline pressure). The last stage was performing field network model calibration to match the overall field performance. During the platform stage calibration, some parameters such as pipeline ID, horizontal flow correlation, friction factor, and holdup factor were fine-tuned to match the platform level operating conditions.\u0000 Most of the wells in J field have been calibrated by meeting the success criterion, which is within +/-5% for the production rates. However, there were some challenges in matching several wells due to well test data validity especially wells located on remote platform where there is no dedicated test separator as well as the impact of gas breakthrough, which may interfere to performance of wells. These wells were decided to be retested in the following month. As for the platform level matching, five platforms were matched within +/-10% against the reported production rates. During the evaluation, it was observed there were some uncertainties in the reported water and gas rates (platform level vs. well test data). This is something that can be looked into for a better measurement in the future. By this observation, it was suggested to select Platform 1 with the most reliable test data as well as the platform rate for the optimization process and qualifying for the field trial. Nevertheless, with the representative network model, two scenarios, reducing separator pressure at platform level and gas lift optimization by an optimal gas lift rate allocation, were performed. T","PeriodicalId":11052,"journal":{"name":"Day 3 Thu, October 14, 2021","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78965156","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}
Treatment of well flow lines with thermochemical/exothermic fluid has shown good results for wax removal compared to conventional hot oil, hot water or solvent treatments. However, the technique has not gained widespread use due to lack sufficient scientific publications that can give more insights over its use and help in designing a safe and effective treatment. This paper presents a novel transient mathematical model for design and analysis of thermochemical treatment for well flow lines by taking into account the chemical kinetics, heat transfer, fusion of wax and associated two-phase flow. The governing equations have been solved using tools of computational fluid dynamics and heat transfer (CFD - HT). The resulting simulator can be used to prepare an optimum thermochemical plan by analysing the effects of important factors including wax details, deposition profile, heat loss, formulation composition and injection strategy. Simulation results with the developed model indicate that entire filling of flowline with thermochemical fluid is not necessary for complete wax removal. Injection of a small thermochemical spacer in the flow line followed by its displacement with crude oil can be suffice in case of short flowlines of onshore fields. Selection of initial reactant concentration and pH has to be done judiciously based on the maximum allowed temperature in the flowline and the desired extent of chemical utilization. A sensitivity analysis has shown the existence of an optimum range of injection rate below which wax removal efficiency is compromised by excessive heat loss and above which it is reduced by insufficient residence time. The major limitation of this technique is encountered for large flowlines where a possibility of re-solidification of removed wax deposits exist due to excessive heat loss. Flowlines of length less than 5 km are found to be ideal candidates as in that case, sufficiently high temperatures can be maintained throughout the journey of thermochemical spacer in the flowline which will prevent re-solidification. The simulator has been validated with field implementation results of two well flow lines where the designed jobs have been successful in removing the entire wax deposits as predicted by the simulator.
{"title":"Novel Simulator for Design and Analysis of Wax Removal Treatment from Well Flow Lines Using Thermochemical Fluids","authors":"M. Qamruzzaman, D. Roy, R. Raman","doi":"10.2118/205754-ms","DOIUrl":"https://doi.org/10.2118/205754-ms","url":null,"abstract":"\u0000 Treatment of well flow lines with thermochemical/exothermic fluid has shown good results for wax removal compared to conventional hot oil, hot water or solvent treatments. However, the technique has not gained widespread use due to lack sufficient scientific publications that can give more insights over its use and help in designing a safe and effective treatment.\u0000 This paper presents a novel transient mathematical model for design and analysis of thermochemical treatment for well flow lines by taking into account the chemical kinetics, heat transfer, fusion of wax and associated two-phase flow. The governing equations have been solved using tools of computational fluid dynamics and heat transfer (CFD - HT). The resulting simulator can be used to prepare an optimum thermochemical plan by analysing the effects of important factors including wax details, deposition profile, heat loss, formulation composition and injection strategy.\u0000 Simulation results with the developed model indicate that entire filling of flowline with thermochemical fluid is not necessary for complete wax removal. Injection of a small thermochemical spacer in the flow line followed by its displacement with crude oil can be suffice in case of short flowlines of onshore fields. Selection of initial reactant concentration and pH has to be done judiciously based on the maximum allowed temperature in the flowline and the desired extent of chemical utilization. A sensitivity analysis has shown the existence of an optimum range of injection rate below which wax removal efficiency is compromised by excessive heat loss and above which it is reduced by insufficient residence time. The major limitation of this technique is encountered for large flowlines where a possibility of re-solidification of removed wax deposits exist due to excessive heat loss. Flowlines of length less than 5 km are found to be ideal candidates as in that case, sufficiently high temperatures can be maintained throughout the journey of thermochemical spacer in the flowline which will prevent re-solidification. The simulator has been validated with field implementation results of two well flow lines where the designed jobs have been successful in removing the entire wax deposits as predicted by the simulator.","PeriodicalId":11052,"journal":{"name":"Day 3 Thu, October 14, 2021","volume":"145 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76520275","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}
Capacitance resistance modeling (CRM) is a data-driven analytical technique for waterflood optimization developed in the early 2000s. The popular implementation uses only production/injection data as input and makes simplifying assumptions of pressure maintenance and injection being the primary driver of production. While these assumptions make CRM a quick plug & play type of technique that can easily be replicated between assets they also lead to major pitfalls, as these assumptions are often invalid. This study explores these pitfalls and discusses workarounds and mitigations to improve the reliability of CRM. CRM was used as a waterflood optimization technique for 3 onshore oil fields, each having 100s of active wells, multiple stacked reservoirs, and over 15 years of pattern waterflood development. The CRM algorithm was implemented in Python and consists of 4 modules: 1) Connectivity solver module – where connectivity between injectors and producers is quantified using a 2 year history match period, 2) Fractional Flow solver module – where oil rates are established as a function of injection rates, 3) Verification module – which is a blind test to assess history match quality, 4) Waterflood optimizer module – which redistributes water between injectors, subject to facility constraints and estimates potential oil gain. Additionally, CRM results were interpreted and validated using an integrated visualization dashboard. The two main issues encountered while using CRM in this study are 1) poor history match (HM) and 2) very high run time in the order of tens of hours due to the large number of wells. Poor HM was attributed to significant noise in the production data, aquifer support contributing to production, well interventions such as water shut-offs, re-perforation, etc. contributing to oil production. These issues were mitigated, and HM was improved using data cleaning techniques such as smoothening, outlier removal, and the usage of pseudo aquifer injectors for material balance. However, these techniques are not foolproof due to the nature of CRM which relies only on trends between producers and injectors for waterflood optimization. Runtime however was reduced to a couple of hours by breaking up the reservoir into sectors and using parallelization.
{"title":"Practical Considerations when Using Capacitance Resistance Modelling CRM for Waterflood Optimization","authors":"Srungeer Simha, Manu Ujjwal, Gaurav Modi","doi":"10.2118/205650-ms","DOIUrl":"https://doi.org/10.2118/205650-ms","url":null,"abstract":"\u0000 Capacitance resistance modeling (CRM) is a data-driven analytical technique for waterflood optimization developed in the early 2000s. The popular implementation uses only production/injection data as input and makes simplifying assumptions of pressure maintenance and injection being the primary driver of production. While these assumptions make CRM a quick plug & play type of technique that can easily be replicated between assets they also lead to major pitfalls, as these assumptions are often invalid. This study explores these pitfalls and discusses workarounds and mitigations to improve the reliability of CRM.\u0000 CRM was used as a waterflood optimization technique for 3 onshore oil fields, each having 100s of active wells, multiple stacked reservoirs, and over 15 years of pattern waterflood development. The CRM algorithm was implemented in Python and consists of 4 modules: 1) Connectivity solver module – where connectivity between injectors and producers is quantified using a 2 year history match period, 2) Fractional Flow solver module – where oil rates are established as a function of injection rates, 3) Verification module – which is a blind test to assess history match quality, 4) Waterflood optimizer module – which redistributes water between injectors, subject to facility constraints and estimates potential oil gain. Additionally, CRM results were interpreted and validated using an integrated visualization dashboard.\u0000 The two main issues encountered while using CRM in this study are 1) poor history match (HM) and 2) very high run time in the order of tens of hours due to the large number of wells. Poor HM was attributed to significant noise in the production data, aquifer support contributing to production, well interventions such as water shut-offs, re-perforation, etc. contributing to oil production. These issues were mitigated, and HM was improved using data cleaning techniques such as smoothening, outlier removal, and the usage of pseudo aquifer injectors for material balance. However, these techniques are not foolproof due to the nature of CRM which relies only on trends between producers and injectors for waterflood optimization. Runtime however was reduced to a couple of hours by breaking up the reservoir into sectors and using parallelization.","PeriodicalId":11052,"journal":{"name":"Day 3 Thu, October 14, 2021","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88058688","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}
A key to successful Well, Reservoir and Facilities Management (WRFM) is to have an up-to-date opportunity funnel. In large mature fields, WRFM opportunity identification is heavily dependent on effective exploitation of measured & interpreted data. This paper presents a suite of data driven workflows, collectively called WRFM Opportunity Finder (WOF), that generates ranked list of opportunities across the WRFM opportunity spectrum. The WOF was developed for a mature waterflooded asset with over 500 active wells and over 30 years of production history. The first step included data collection and cleanup using python routines and its integration into an interactive visualization dashboard. The WOF used this data to generate ranked list of following opportunity types: (a) Bean-up/bean-down candidates (b) Watershut-off candidates (c) Add-perf candidates (d) PLT/ILT data gathering candidates, and (e) well stimulation candidates. The WOF algorithms, implemented using python, largely comprised of rule-based workflows with occasional use of machine learning in intermediate steps. In a large mature asset, field/reservoir/well reviews are typically conducted area by area or reservoir by reservoir and is therefore a slow process. It is challenging to have an updated holistic overview of opportunities across the field which can allow prioritization of optimal opportunities. Though the opportunity screening logic may be linked to clear physics-based rules, its maturation is often difficult as it requires processing and integration of large volumes of multi-disciplinary data through laborious manual review processes. The WOF addressed these issues by leveraging data processing algorithms that gathered data directly from databases and applied customized data processing routines. This led to reduction in data preparation and integration time by 90%. The WOF used workflows linked to petroleum engineering principles to arrive at ranked lists of opportunities with a potential to add 1-2% increment in oil production. The integrated visualization dashboard allowed quick and transparent validation of the identified opportunities and their ranking basis using a variety of independent checks. The results from WOF will inform a range of business delivery elements such as workover & data gathering plan, exception-based-surveillance and facilities debottlenecking plan. WOF exploits the best of both worlds - physics-based solutions and data driven techniques. It offers transparent logic which are scalable and replicable to a variety of settings and hence has an edge over pure machine learning approaches. The WOF accelerates identification of low capex/no-capex opportunities using existing data. It promotes maximization of returns on already made investments and hence lends resilience to business in the low oil price environment.
{"title":"Enhancing Well, Reservoir and Facilities Management WRFM Opportunity Identification with Data Driven Techniques","authors":"Manu Ujjwal, Gaurav Modi, Srungeer Simha","doi":"10.2118/205596-ms","DOIUrl":"https://doi.org/10.2118/205596-ms","url":null,"abstract":"\u0000 A key to successful Well, Reservoir and Facilities Management (WRFM) is to have an up-to-date opportunity funnel. In large mature fields, WRFM opportunity identification is heavily dependent on effective exploitation of measured & interpreted data. This paper presents a suite of data driven workflows, collectively called WRFM Opportunity Finder (WOF), that generates ranked list of opportunities across the WRFM opportunity spectrum.\u0000 The WOF was developed for a mature waterflooded asset with over 500 active wells and over 30 years of production history. The first step included data collection and cleanup using python routines and its integration into an interactive visualization dashboard. The WOF used this data to generate ranked list of following opportunity types: (a) Bean-up/bean-down candidates (b) Watershut-off candidates (c) Add-perf candidates (d) PLT/ILT data gathering candidates, and (e) well stimulation candidates. The WOF algorithms, implemented using python, largely comprised of rule-based workflows with occasional use of machine learning in intermediate steps.\u0000 In a large mature asset, field/reservoir/well reviews are typically conducted area by area or reservoir by reservoir and is therefore a slow process. It is challenging to have an updated holistic overview of opportunities across the field which can allow prioritization of optimal opportunities. Though the opportunity screening logic may be linked to clear physics-based rules, its maturation is often difficult as it requires processing and integration of large volumes of multi-disciplinary data through laborious manual review processes. The WOF addressed these issues by leveraging data processing algorithms that gathered data directly from databases and applied customized data processing routines. This led to reduction in data preparation and integration time by 90%. The WOF used workflows linked to petroleum engineering principles to arrive at ranked lists of opportunities with a potential to add 1-2% increment in oil production. The integrated visualization dashboard allowed quick and transparent validation of the identified opportunities and their ranking basis using a variety of independent checks. The results from WOF will inform a range of business delivery elements such as workover & data gathering plan, exception-based-surveillance and facilities debottlenecking plan.\u0000 WOF exploits the best of both worlds - physics-based solutions and data driven techniques. It offers transparent logic which are scalable and replicable to a variety of settings and hence has an edge over pure machine learning approaches. The WOF accelerates identification of low capex/no-capex opportunities using existing data. It promotes maximization of returns on already made investments and hence lends resilience to business in the low oil price environment.","PeriodicalId":11052,"journal":{"name":"Day 3 Thu, October 14, 2021","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80628365","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}
B. Widyoko, Patria Indrayana, Toto Hutabarat, A. Budiarko, Mitterank Siboro, Henricus Herwin
Mahakam Contract Area is located in East Kalimantan Province, Indonesia. It covers an operating area of 3,266 km2, and consists of 7 producing fields. Most of Mahakam hydrocarbon accumulations are located below body of water, with wellhead production facilities installed in the estuary of Mahakam river (referred as swamp area, 0 to 5m water depth) and the western part of Makassar Strait (referred as offshore area, 30 to 70 m water depth). Mahakam production history goes as far back as mid 1970s with production of Handil and Bekapai oil fields. Gas production started by the decade of 1990s along with emergence of LNG trading, supplying Bontang LNG plant, through production of 2 giant gas fields: Tunu and Peciko, and smaller Tambora field. In the mid 2000s, Mahakam attained its peak gas production in the level of 2,600 MMscfd and was Indonesia's biggest gas producer. Two remaining gas discoveries, Sisi Nubi and South Mahakam, were put in production respectively in 2007 and 2012. Due to absence of new discoveries and new fields brought into production, Mahakam production has entered decline phase since 2010, and by end of 2020, after 46 years of production, the production is in the level of 600 MMscfd. In 2018, along with the expiration of Mahakam production sharing contract, Pertamina Hulu Mahakam (PHM), a subsidiary of Indonesian national energy company, Pertamina, was awarded operatorship of Mahakam Block. This paper describes the efforts undertaken by PHM to fight production decline and rejuvenate development portfolio, with focus on expanding subsurface development portfolio and reserves renewal by optimizing development concept and cost through fit-for-purpose design, innovation, and full cycle value engineering.
{"title":"Enhancing Economics of Resources Development of Mature Mahakam Fields Through Innovation, Design Optimization, and Value Engineering","authors":"B. Widyoko, Patria Indrayana, Toto Hutabarat, A. Budiarko, Mitterank Siboro, Henricus Herwin","doi":"10.2118/205713-ms","DOIUrl":"https://doi.org/10.2118/205713-ms","url":null,"abstract":"Mahakam Contract Area is located in East Kalimantan Province, Indonesia. It covers an operating area of 3,266 km2, and consists of 7 producing fields. Most of Mahakam hydrocarbon accumulations are located below body of water, with wellhead production facilities installed in the estuary of Mahakam river (referred as swamp area, 0 to 5m water depth) and the western part of Makassar Strait (referred as offshore area, 30 to 70 m water depth).\u0000 Mahakam production history goes as far back as mid 1970s with production of Handil and Bekapai oil fields. Gas production started by the decade of 1990s along with emergence of LNG trading, supplying Bontang LNG plant, through production of 2 giant gas fields: Tunu and Peciko, and smaller Tambora field. In the mid 2000s, Mahakam attained its peak gas production in the level of 2,600 MMscfd and was Indonesia's biggest gas producer. Two remaining gas discoveries, Sisi Nubi and South Mahakam, were put in production respectively in 2007 and 2012. Due to absence of new discoveries and new fields brought into production, Mahakam production has entered decline phase since 2010, and by end of 2020, after 46 years of production, the production is in the level of 600 MMscfd.\u0000 In 2018, along with the expiration of Mahakam production sharing contract, Pertamina Hulu Mahakam (PHM), a subsidiary of Indonesian national energy company, Pertamina, was awarded operatorship of Mahakam Block. This paper describes the efforts undertaken by PHM to fight production decline and rejuvenate development portfolio, with focus on expanding subsurface development portfolio and reserves renewal by optimizing development concept and cost through fit-for-purpose design, innovation, and full cycle value engineering.","PeriodicalId":11052,"journal":{"name":"Day 3 Thu, October 14, 2021","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84205326","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}
C. Temizel, C. H. Canbaz, Yildiray Palabiyik, Hakki Aydin, M. Tran, M. H. Ozyurtkan, M. Yurukcu, Paul Johnson
Reservoir engineering constitutes a major part of the studies regarding oil and gas exploration and production. Reservoir engineering has various duties, including conducting experiments, constructing appropriate models, characterization, and forecasting reservoir dynamics. However, traditional engineering approaches started to face challenges as the number of raw field data increases. It pushed the researchers to use more powerful tools for data classification, cleaning and preparing data to be used in models, which enhances a better data evaluation, thus making proper decisions. In addition, simultaneous simulations are sometimes performed, aiming to have optimization and sensitivity analysis during the history matching process. Multi-functional works are required to meet all these deficiencies. Upgrading conventional reservoir engineering approaches with CPUs, or more powerful computers are insufficient since it increases computational cost and is time-consuming. Machine learning techniques have been proposed as the best solution for strong learning capability and computational efficiency. Recently developed algorithms make it possible to handle a very large number of data with high accuracy. The most widely used machine learning approaches are: Artificial Neural Network (ANN), Support Vector Machines and Adaptive Neuro-Fuzzy Inference Systems. In this study, these approaches are introduced by providing their capability and limitations. After that, the study focuses on using machine learning techniques in unconventional reservoir engineering calculations: Reservoir characterization, PVT calculations and optimization of well completion. These processes are repeated until all the values reach to the output layer. Normally, one hidden layer is good enough for most problems and additional hidden layers usually does not improve the model performance, instead, it may create the risk for converging to a local minimum and make the model more complex. The most typical neural network is the forward feed network, often used for data classification. MLP has a learning function that minimizes a global error function, the least square method. It uses back propagation algorithm to update the weights, searching for local minima by performing a gradient descent (Figure 1). The learning rate is usually selected as less than one.
{"title":"A Thorough Review of Machine Learning Applications in Oil and Gas Industry","authors":"C. Temizel, C. H. Canbaz, Yildiray Palabiyik, Hakki Aydin, M. Tran, M. H. Ozyurtkan, M. Yurukcu, Paul Johnson","doi":"10.2118/205720-ms","DOIUrl":"https://doi.org/10.2118/205720-ms","url":null,"abstract":"\u0000 Reservoir engineering constitutes a major part of the studies regarding oil and gas exploration and production. Reservoir engineering has various duties, including conducting experiments, constructing appropriate models, characterization, and forecasting reservoir dynamics. However, traditional engineering approaches started to face challenges as the number of raw field data increases. It pushed the researchers to use more powerful tools for data classification, cleaning and preparing data to be used in models, which enhances a better data evaluation, thus making proper decisions. In addition, simultaneous simulations are sometimes performed, aiming to have optimization and sensitivity analysis during the history matching process. Multi-functional works are required to meet all these deficiencies. Upgrading conventional reservoir engineering approaches with CPUs, or more powerful computers are insufficient since it increases computational cost and is time-consuming. Machine learning techniques have been proposed as the best solution for strong learning capability and computational efficiency. Recently developed algorithms make it possible to handle a very large number of data with high accuracy. The most widely used machine learning approaches are: Artificial Neural Network (ANN), Support Vector Machines and Adaptive Neuro-Fuzzy Inference Systems. In this study, these approaches are introduced by providing their capability and limitations. After that, the study focuses on using machine learning techniques in unconventional reservoir engineering calculations: Reservoir characterization, PVT calculations and optimization of well completion.\u0000 These processes are repeated until all the values reach to the output layer. Normally, one hidden layer is good enough for most problems and additional hidden layers usually does not improve the model performance, instead, it may create the risk for converging to a local minimum and make the model more complex. The most typical neural network is the forward feed network, often used for data classification. MLP has a learning function that minimizes a global error function, the least square method. It uses back propagation algorithm to update the weights, searching for local minima by performing a gradient descent (Figure 1). The learning rate is usually selected as less than one.","PeriodicalId":11052,"journal":{"name":"Day 3 Thu, October 14, 2021","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80443289","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}
R. Ramdzani, O. Talabi, Adeline Siaw Hui Chua, Edwin Lawrence
Field X located in offshore South East Asia, is a deepwater, turbidite natural gas greenfield currently being developed using a subsea tieback production system. It is part of a group of fields anticipated to be developed together as a cluster. Due to the nature of this development, several key challenges were foreseen: i) subsurface uncertainty ii) production network impact on system deliverability and flow assurance iii) efficient use of high frequency data in managing production. The objective of this study was to demonstrate a flexible and robust methodology to address these challenges by integrating multiple realizations of the reservoir model with surface network models and showing how this could be link to "live" production data in the future. This paper describes the development and deployment of the solutions to overcome those challenges. Furthermore, the paper describes the results and key observations for further recommendation in moving forward to field digitalization. The process started with a quality check of the base case dynamic reservoir model to improve performance and enable multiple realization runs in a reasonable timeframe. This was followed by sensitivity and uncertainty analysis to obtain 10 realizations of the subsurface model which were integrated with the steady-state surface network model. Optimization under uncertainty was then performed on the integrated model to evaluate three illustrative development scenarios. To demonstrate extensibility, two additional candidate reservoirs for future development were also tied in to the system and modelled as a single integrated asset model to meet the anticipated gas delivery targets. Next, the subsurface model was integrated with a multiphase transient network model to show how it can be used to evaluate the risk of hydrate formation along the pipeline during planned production start-up. As a final step, in-built application programming interface (API) in the integration software was used to perform automation, enabling the integrated model to be activated and run automatically while being updated with sample "live" production data. At the conclusion of the study, the reservoir simulation performance was improved, reducing runtime by a factor of four without significant change in base case results. The results of the coupled reservoir to steady-state network simulation and optimization showed that the network could constrain reservoir deliverability by up to 4% in all realizations due to back pressure, and the most optimum development scenario was to delay first gas production and operate with shorter duration at high separator pressure. With the additional reservoirs in the integrated model, the production plateau could be extended up to 15 years beyond the base case without exceeding the specified water handling limit. For hydrates risk analysis, the differences between hydrate formation and fluid temperature indicated there was a potential risk of hydrate formation, w
{"title":"Laying the Foundations of a Digital Gas Field Development in a Greenfield Cluster Using Integrated Modelling: A Case Study","authors":"R. Ramdzani, O. Talabi, Adeline Siaw Hui Chua, Edwin Lawrence","doi":"10.2118/205585-ms","DOIUrl":"https://doi.org/10.2118/205585-ms","url":null,"abstract":"\u0000 Field X located in offshore South East Asia, is a deepwater, turbidite natural gas greenfield currently being developed using a subsea tieback production system. It is part of a group of fields anticipated to be developed together as a cluster. Due to the nature of this development, several key challenges were foreseen: i) subsurface uncertainty ii) production network impact on system deliverability and flow assurance iii) efficient use of high frequency data in managing production. The objective of this study was to demonstrate a flexible and robust methodology to address these challenges by integrating multiple realizations of the reservoir model with surface network models and showing how this could be link to \"live\" production data in the future.\u0000 This paper describes the development and deployment of the solutions to overcome those challenges. Furthermore, the paper describes the results and key observations for further recommendation in moving forward to field digitalization.\u0000 The process started with a quality check of the base case dynamic reservoir model to improve performance and enable multiple realization runs in a reasonable timeframe. This was followed by sensitivity and uncertainty analysis to obtain 10 realizations of the subsurface model which were integrated with the steady-state surface network model. Optimization under uncertainty was then performed on the integrated model to evaluate three illustrative development scenarios. To demonstrate extensibility, two additional candidate reservoirs for future development were also tied in to the system and modelled as a single integrated asset model to meet the anticipated gas delivery targets. Next, the subsurface model was integrated with a multiphase transient network model to show how it can be used to evaluate the risk of hydrate formation along the pipeline during planned production start-up. As a final step, in-built application programming interface (API) in the integration software was used to perform automation, enabling the integrated model to be activated and run automatically while being updated with sample \"live\" production data.\u0000 At the conclusion of the study, the reservoir simulation performance was improved, reducing runtime by a factor of four without significant change in base case results. The results of the coupled reservoir to steady-state network simulation and optimization showed that the network could constrain reservoir deliverability by up to 4% in all realizations due to back pressure, and the most optimum development scenario was to delay first gas production and operate with shorter duration at high separator pressure. With the additional reservoirs in the integrated model, the production plateau could be extended up to 15 years beyond the base case without exceeding the specified water handling limit. For hydrates risk analysis, the differences between hydrate formation and fluid temperature indicated there was a potential risk of hydrate formation, w","PeriodicalId":11052,"journal":{"name":"Day 3 Thu, October 14, 2021","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86594763","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}
Heri Tanjung, Ratna Dewanda, Irzal Irzal, Sakti Parsaulian, Adhitya Pratama Lanadito, E. F. Butarbutar, Herbert Sipahutar, Sofyan Sumarna, Muhammad Aldie Syafaat, A. T. Suherman, Muhammad Subhan, Iwan Abdurrahman, I. K. Barus, Mochamad Riza Zakaria, M. Naiola, Mohammad Wildan Alfian, Danto Prihandono, Rizky Sulaksono, Zeppy Irwanzah Budiarto, Rifky Tri Putra, D. Pramudito, Heri Suryadi, Rakhmadian Abdillah
Freshwater environment and high clay content are quite common in Indonesia. This introduces certain challenge in performing hydrocarbon identification and evaluation especially in already cased wells. In old producer wells, possible conditions such as fluid channeling behind casing and trapped hydrocarbon in annulus add more complexity in performing behind casing analysis to understand current reservoir condition. In order to increase the success in finding remaining hydrocarbon potential, PERTAMINA has deployed pulsed neutron logs (PNL) to accurately pinpoint the targeted interval for perforation. Since 2017, the PNL campaign has covered approximately 160 wells in PERTAMINA's development fields across Indonesia up until now. PNL service offers nuclear-based statistical measurement such as sigma, thermal neutron decay porosity (TPHI), and carbon-oxygen yield that allows simultaneous oil and gas saturation evaluation without any dependence on water salinity and other electrical properties of the formation and fluid. It also allows computation of elemental dry weight from elemental spectroscopy data which can be utilized to determine lithology to complement the standard open-hole logs dataset. The more advanced PNL tool raises the bar even further by offering new measurement of fast neutron capture cross section (FNXS) log which is useful to identify gas even in tight rock formation. The latest generation also features self-compensation algorithm resulting in more robust TPHI and sigma log under complex circumstances such as multi-casing/tubing. This paper showcases several prominent success stories of oil and gas findings identified from PNL interpretation in development wells. There are also several examples of elemental spectroscopy data utilization from PNL to prevent non-economical perforation by means of providing accurate lithology and porosity analysis as compared to previous result built from old and/or incomplete open-hole logs dataset. This PNL campaign has also given valuable insights of borehole and reservoir condition which might have been overlooked such as hydrocarbon in annulus, low pressure gas zone identification and batman's ear boundary effect. Low pressure gas zone may be qualitatively identified whenever TPHI from PNL is noticeably lower than neutron porosity measurement from the open-hole log. Batman's ear effect is usually observed when a body of sand is sandwiched between carbonaceous shales or coal layers resulting successive oil-water-oil saturation profile in one homogenous body of sand, shown as oil peaks at the bed boundaries similar with the appearance of batman's ear. As the sand gets thinner, these two oil peaks might merge into one solid body of high oil saturation which might not depict the true oil potential of the sand.
{"title":"Fashioning the Increase of Oil and Gas Production through Advanced Cased Hole Formation Evaluation","authors":"Heri Tanjung, Ratna Dewanda, Irzal Irzal, Sakti Parsaulian, Adhitya Pratama Lanadito, E. F. Butarbutar, Herbert Sipahutar, Sofyan Sumarna, Muhammad Aldie Syafaat, A. T. Suherman, Muhammad Subhan, Iwan Abdurrahman, I. K. Barus, Mochamad Riza Zakaria, M. Naiola, Mohammad Wildan Alfian, Danto Prihandono, Rizky Sulaksono, Zeppy Irwanzah Budiarto, Rifky Tri Putra, D. Pramudito, Heri Suryadi, Rakhmadian Abdillah","doi":"10.2118/205679-ms","DOIUrl":"https://doi.org/10.2118/205679-ms","url":null,"abstract":"\u0000 Freshwater environment and high clay content are quite common in Indonesia. This introduces certain challenge in performing hydrocarbon identification and evaluation especially in already cased wells. In old producer wells, possible conditions such as fluid channeling behind casing and trapped hydrocarbon in annulus add more complexity in performing behind casing analysis to understand current reservoir condition. In order to increase the success in finding remaining hydrocarbon potential, PERTAMINA has deployed pulsed neutron logs (PNL) to accurately pinpoint the targeted interval for perforation. Since 2017, the PNL campaign has covered approximately 160 wells in PERTAMINA's development fields across Indonesia up until now.\u0000 PNL service offers nuclear-based statistical measurement such as sigma, thermal neutron decay porosity (TPHI), and carbon-oxygen yield that allows simultaneous oil and gas saturation evaluation without any dependence on water salinity and other electrical properties of the formation and fluid. It also allows computation of elemental dry weight from elemental spectroscopy data which can be utilized to determine lithology to complement the standard open-hole logs dataset. The more advanced PNL tool raises the bar even further by offering new measurement of fast neutron capture cross section (FNXS) log which is useful to identify gas even in tight rock formation. The latest generation also features self-compensation algorithm resulting in more robust TPHI and sigma log under complex circumstances such as multi-casing/tubing.\u0000 This paper showcases several prominent success stories of oil and gas findings identified from PNL interpretation in development wells. There are also several examples of elemental spectroscopy data utilization from PNL to prevent non-economical perforation by means of providing accurate lithology and porosity analysis as compared to previous result built from old and/or incomplete open-hole logs dataset.\u0000 This PNL campaign has also given valuable insights of borehole and reservoir condition which might have been overlooked such as hydrocarbon in annulus, low pressure gas zone identification and batman's ear boundary effect. Low pressure gas zone may be qualitatively identified whenever TPHI from PNL is noticeably lower than neutron porosity measurement from the open-hole log. Batman's ear effect is usually observed when a body of sand is sandwiched between carbonaceous shales or coal layers resulting successive oil-water-oil saturation profile in one homogenous body of sand, shown as oil peaks at the bed boundaries similar with the appearance of batman's ear. As the sand gets thinner, these two oil peaks might merge into one solid body of high oil saturation which might not depict the true oil potential of the sand.","PeriodicalId":11052,"journal":{"name":"Day 3 Thu, October 14, 2021","volume":"154 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86647033","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}
Short Term Injection Re-distribution (STIR) is a python based real-time WaterFlood optimization technique for brownfield assets that uses advanced data analytics. The objective of this technique is to generate recommendations for injection water re-distribution to maximize oil production at the facility level. Even though this is a data driven technique, it is tightly bounded by Petroleum Engineering principles such as material balance etc. The workflow integrates and analyse short term data (last 3-6 months) at reservoir, wells and facility level. STIR workflow is divided into three modules: Injector-producer connectivity Injector efficiency Injection water optimization First module uses four major data types to estimate the connectivity between each injector-producer pair in the reservoir: Producers data (pressure, WC, GOR, salinity) Faults presence Subsurface distance Perforation similarity – layers and kh Second module uses connectivity and watercut data to establish the injector efficiency. Higher efficiency injectors contribute most to production while poor efficiency injectors contribute to water recycling. Third module has a mathematical optimizer to maximize the oil production by re-distributing the injection water amongst injectors while honoring the constraints at each node (well, facility etc.) of the production system. The STIR workflow has been applied to 6 reservoirs across different assets and an annual increase of 3-7% in oil production is predicted. Each recommendation is verified using an independent source of data and hence, the generated recommendations align very well with the reservoir understanding. The benefits of this technique can be seen in 3-6 months of implementation in terms of increased oil production and better support (pressure increase) to low watercut producers. The inherent flexibility in the workflow allows for easy replication in any Waterflooded Reservoir and works best when the injector well count in the reservoir is relatively high. Geological features are well represented in the workflow which is one of the unique functionalities of this technique. This method also generates producers bean-up and injector stimulation candidates opportunities. This low cost (no CAPEX) technique offers the advantages of conventional petroleum engineering techniques and Data driven approach. This technique provides a great alternative for WaterFlood management in brownfield where performing a reliable conventional analysis is challenging or at times impossible. STIR can be implemented in a reservoir from scratch in 3-6 weeks timeframe.
{"title":"Short Term Injection Re-Distribution STIR: Real-Time Waterflood Optimization Technique Using Advanced Data Analytics","authors":"Gaurav Modi, Manu Ujjwal, Srungeer Simha","doi":"10.2118/205593-ms","DOIUrl":"https://doi.org/10.2118/205593-ms","url":null,"abstract":"\u0000 Short Term Injection Re-distribution (STIR) is a python based real-time WaterFlood optimization technique for brownfield assets that uses advanced data analytics. The objective of this technique is to generate recommendations for injection water re-distribution to maximize oil production at the facility level. Even though this is a data driven technique, it is tightly bounded by Petroleum Engineering principles such as material balance etc. The workflow integrates and analyse short term data (last 3-6 months) at reservoir, wells and facility level.\u0000 STIR workflow is divided into three modules:\u0000 Injector-producer connectivity Injector efficiency Injection water optimization\u0000 First module uses four major data types to estimate the connectivity between each injector-producer pair in the reservoir:\u0000 Producers data (pressure, WC, GOR, salinity) Faults presence Subsurface distance Perforation similarity – layers and kh\u0000 Second module uses connectivity and watercut data to establish the injector efficiency. Higher efficiency injectors contribute most to production while poor efficiency injectors contribute to water recycling.\u0000 Third module has a mathematical optimizer to maximize the oil production by re-distributing the injection water amongst injectors while honoring the constraints at each node (well, facility etc.) of the production system.\u0000 The STIR workflow has been applied to 6 reservoirs across different assets and an annual increase of 3-7% in oil production is predicted. Each recommendation is verified using an independent source of data and hence, the generated recommendations align very well with the reservoir understanding. The benefits of this technique can be seen in 3-6 months of implementation in terms of increased oil production and better support (pressure increase) to low watercut producers. The inherent flexibility in the workflow allows for easy replication in any Waterflooded Reservoir and works best when the injector well count in the reservoir is relatively high. Geological features are well represented in the workflow which is one of the unique functionalities of this technique. This method also generates producers bean-up and injector stimulation candidates opportunities.\u0000 This low cost (no CAPEX) technique offers the advantages of conventional petroleum engineering techniques and Data driven approach. This technique provides a great alternative for WaterFlood management in brownfield where performing a reliable conventional analysis is challenging or at times impossible. STIR can be implemented in a reservoir from scratch in 3-6 weeks timeframe.","PeriodicalId":11052,"journal":{"name":"Day 3 Thu, October 14, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86174891","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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