H. Saradva, Siddharth Jain, M. Hamadi, K. Thakur, G. Govindan, A. F. Ahmed
This paper presents a case study from Onshore wells in Sharjah, UAE on investigating liquid loading in 5 multilateral gas wells having various trajectories ranging from toe-up, toe-down and hybrid openhole legs. These wells are subjected to wellhead pressure reduction to maximize production rates. The main objective of the study was to evaluate the production performance for different completion designs with respect to liquid loading onset and overall production assessment with declining reservoir pressure. Dynamic multiphase flow simulator was used to conduct this study to accurately capture the details of the multilaterals system and its complex trajectories. The first step involved validating the well model with reasonable history match between the simulation and actual production data. The validated model then was used as a basis for predicting the liquid loading onset point for a given reservoir pressure decline. Multiple cases were investigated to evaluate various completion options (i.e. with or without tubing) to determine how and when the liquid loading occurs at different laterals with varying lateral trajectory. This study has showed that in such complex multi-lateral wells, laterals load up at different points in time and reservoir pressures, being affected mainly by the geometry and orientation of lateral and the production contribution. Moreover, installing tubing in these wells had the opposite anticipated effect on liquid loading by accelerating the liquid loading onset in the laterals due to the imposed additional restriction. Generally, toe-down trajectory tends to have thicker liquid film and a potential for reduced flow contribution due to liquid accumulation at the toe. These wells have a fishbone openhole multilateral network with comingled flow in the vertical section. It is observed that production tubing in the vertical section provides friction that accelerates the onset of liquid loading and hence results in decreased production for wells operating in very low reservoir pressure range. Based on overall production assessment ‘no tubing’ scenario would be more beneficial. Further, the timing of implementation of the tubing restriction later in the field life can be selected based on dynamic simulations (also evaluating economic constraints vs production gain). Transient mechanistic flow model captures the liquid loading phenomena by film reversal which usually occurs before the critical rate limit based on droplet drag forces assessment. Further, liquid loading onset occurs in the laterals first rather than the tubing section which reduces the applicability of conventional nodal analysis tools. Evaluating liquid loading behaviour in such multilateral wells with proper dynamic simulation is critical for understanding the laterals behaviour and therefore optimizing the production performance to maximize the wells uptime and ultimately the overall gas recovery as well as optimal usage of CAPEX.
{"title":"Evaluating Liquid Loading Using Multiphase Dynamic Flow Simulation in Complex Openhole Multilateral Gas Condensate Wells","authors":"H. Saradva, Siddharth Jain, M. Hamadi, K. Thakur, G. Govindan, A. F. Ahmed","doi":"10.2118/194868-MS","DOIUrl":"https://doi.org/10.2118/194868-MS","url":null,"abstract":"\u0000 This paper presents a case study from Onshore wells in Sharjah, UAE on investigating liquid loading in 5 multilateral gas wells having various trajectories ranging from toe-up, toe-down and hybrid openhole legs. These wells are subjected to wellhead pressure reduction to maximize production rates. The main objective of the study was to evaluate the production performance for different completion designs with respect to liquid loading onset and overall production assessment with declining reservoir pressure.\u0000 Dynamic multiphase flow simulator was used to conduct this study to accurately capture the details of the multilaterals system and its complex trajectories. The first step involved validating the well model with reasonable history match between the simulation and actual production data. The validated model then was used as a basis for predicting the liquid loading onset point for a given reservoir pressure decline. Multiple cases were investigated to evaluate various completion options (i.e. with or without tubing) to determine how and when the liquid loading occurs at different laterals with varying lateral trajectory.\u0000 This study has showed that in such complex multi-lateral wells, laterals load up at different points in time and reservoir pressures, being affected mainly by the geometry and orientation of lateral and the production contribution. Moreover, installing tubing in these wells had the opposite anticipated effect on liquid loading by accelerating the liquid loading onset in the laterals due to the imposed additional restriction. Generally, toe-down trajectory tends to have thicker liquid film and a potential for reduced flow contribution due to liquid accumulation at the toe.\u0000 These wells have a fishbone openhole multilateral network with comingled flow in the vertical section. It is observed that production tubing in the vertical section provides friction that accelerates the onset of liquid loading and hence results in decreased production for wells operating in very low reservoir pressure range. Based on overall production assessment ‘no tubing’ scenario would be more beneficial. Further, the timing of implementation of the tubing restriction later in the field life can be selected based on dynamic simulations (also evaluating economic constraints vs production gain).\u0000 Transient mechanistic flow model captures the liquid loading phenomena by film reversal which usually occurs before the critical rate limit based on droplet drag forces assessment. Further, liquid loading onset occurs in the laterals first rather than the tubing section which reduces the applicability of conventional nodal analysis tools. Evaluating liquid loading behaviour in such multilateral wells with proper dynamic simulation is critical for understanding the laterals behaviour and therefore optimizing the production performance to maximize the wells uptime and ultimately the overall gas recovery as well as optimal usage of CAPEX.","PeriodicalId":11031,"journal":{"name":"Day 4 Thu, March 21, 2019","volume":"105 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78750214","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}
J. Shah, Nur Athirah Dahlan, M. Kamarulzaman, M. A. N. C. A. Razak, Junirda Jamaluddin
Low Resistivity low contrast (LRLC) reservoirs were normally disregarded due to high water saturation and classified as tight sand. LRLC reservoir defined as Pay that has low resistivity contrast between sand and adjacent shale due to presence of conductive mineral or fresh water. Hence, this paper will transform the standpoint by demonstrating values and potential reserve addition underneath LRLC reservoir which proves that it could contribute equally as the conventional reservoir and realizing potential reserve growth. HY field located in Baram Delta Basin East Malaysia has been producing for more than 40 years and classified as lower coastal plain to coastal environment. The reservoir is loosely consolidated, fine to very fine sandstone and interbedded with shale. Z reservoir (Low Resistivity contrast reservoir) initially identified as gas-bearing reservoir with fresh water salinity of 2k-4kppm. Plus, difference in resistivity values between sand and adjacent shale only separated by ~3ohmm .Due to these claims, there is no Oil interpreted below the gas level and been neglected for years. A robust water salinity investigation supported with the geological point of view and water sample taken at the wellhead, Project Team proposed the water salinity should be 10k-15k ppm which is more saline than previously assumed. Revision in water salinity value has led to pinpoint Z reservoir as Oil bearing reservoir and recover estimated ~200 ft Pay of Oil column in Z reservoir. An appraisal well was drilled for data gathering and exploring potential in the deeper sections, hence serve as a platform for further petrophysical evaluation in the Z reservoir. As a result, Project team managed to take Oil sample and Oil gradient for Z reservoir. In addition, PVT lab result showed the oil sample taken having similar fluid property as the produced oil in the major reservoir. Based from the existing static model, potential additional of recoverable reserves was calculated around 20 MMstb for the Z reservoir. This has been an eye opener for the team to give an extra attention and emphasis on the true potential beneath the LRLC reservoir.
{"title":"The Revelation of Minor Reservoir Opportunity: Realizing Low Resistivity Contrast Reservoir Play Type in Baram Delta Basin East Malaysia, Thru REM Log Enhancement and Comprehensive Water Salinity Analysis","authors":"J. Shah, Nur Athirah Dahlan, M. Kamarulzaman, M. A. N. C. A. Razak, Junirda Jamaluddin","doi":"10.2118/194917-MS","DOIUrl":"https://doi.org/10.2118/194917-MS","url":null,"abstract":"\u0000 Low Resistivity low contrast (LRLC) reservoirs were normally disregarded due to high water saturation and classified as tight sand. LRLC reservoir defined as Pay that has low resistivity contrast between sand and adjacent shale due to presence of conductive mineral or fresh water. Hence, this paper will transform the standpoint by demonstrating values and potential reserve addition underneath LRLC reservoir which proves that it could contribute equally as the conventional reservoir and realizing potential reserve growth.\u0000 HY field located in Baram Delta Basin East Malaysia has been producing for more than 40 years and classified as lower coastal plain to coastal environment. The reservoir is loosely consolidated, fine to very fine sandstone and interbedded with shale. Z reservoir (Low Resistivity contrast reservoir) initially identified as gas-bearing reservoir with fresh water salinity of 2k-4kppm. Plus, difference in resistivity values between sand and adjacent shale only separated by ~3ohmm .Due to these claims, there is no Oil interpreted below the gas level and been neglected for years.\u0000 A robust water salinity investigation supported with the geological point of view and water sample taken at the wellhead, Project Team proposed the water salinity should be 10k-15k ppm which is more saline than previously assumed. Revision in water salinity value has led to pinpoint Z reservoir as Oil bearing reservoir and recover estimated ~200 ft Pay of Oil column in Z reservoir.\u0000 An appraisal well was drilled for data gathering and exploring potential in the deeper sections, hence serve as a platform for further petrophysical evaluation in the Z reservoir. As a result, Project team managed to take Oil sample and Oil gradient for Z reservoir. In addition, PVT lab result showed the oil sample taken having similar fluid property as the produced oil in the major reservoir. Based from the existing static model, potential additional of recoverable reserves was calculated around 20 MMstb for the Z reservoir. This has been an eye opener for the team to give an extra attention and emphasis on the true potential beneath the LRLC reservoir.","PeriodicalId":11031,"journal":{"name":"Day 4 Thu, March 21, 2019","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77325697","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}
Petrophysics is a pivotal discipline that bridges engineering and geosciences for reservoir characterization and development. New sensor technologies have enabled real-time streaming of large-volume, multi-scale, and high-dimensional petrophysical data into our databases. Petrophysical data types are extremely diverse, and include numeric curves, arrays, waveforms, images, maps, 3-D volumes, and texts. All data can be indexed with depth (continuous or discrete) or time. Petrophysical data exhibits all the "7V" characteristics of big data, i.e., volume, velocity, variety, variability, veracity, visualization, and value. This paper will give an overview of both theories and applications of machine learning methods as applicable to petrophysical big data analysis. Recent publications indicate that petrophysical data-driven analytics (PDDA) has been emerging as an active sub-discipline of petrophysics. Field examples from the petrophysics literature will be used to illustrate the advantages of machine learning in the following technical areas: (1) Geological facies classification or petrophysical rock typing; (2) Seismic rock properties or rock physics modeling; (3) Petrophysical/geochemical/geomechanical properties prediction; (3) Fast physical modeling of logging tools; (4) Well and reservoir surveillance; (6) Automated data quality control; (7) Pseudo data generation; and (8) Logging or coring operation guidance. The paper will also review the major challenges that need to be overcome before the potentially game-changing value of machine learning for petrophysics discipline can be realized. First, a robust theoretical foundation to support the application of machine leaning to petrophysical interpretation should be established; second, the utility of existing machine learning algorithms must be evaluated and tested in different petrophysical tasks with different data scenarios; third, procedures to control the quality of data used in machine leaning algorithms need to be implemented and the associated uncertainties need to be appropriately addressed. The paper will outlook the future opportunities of enabling advanced data analytics to solve challenging oilfield problems in the era of the 4th industrial revolution (IR4.0).
{"title":"When Petrophysics Meets Big Data: What can Machine Do?","authors":"Chicheng Xu, S. Misra, P. Srinivasan, S. Ma","doi":"10.2118/195068-MS","DOIUrl":"https://doi.org/10.2118/195068-MS","url":null,"abstract":"\u0000 Petrophysics is a pivotal discipline that bridges engineering and geosciences for reservoir characterization and development. New sensor technologies have enabled real-time streaming of large-volume, multi-scale, and high-dimensional petrophysical data into our databases. Petrophysical data types are extremely diverse, and include numeric curves, arrays, waveforms, images, maps, 3-D volumes, and texts. All data can be indexed with depth (continuous or discrete) or time. Petrophysical data exhibits all the \"7V\" characteristics of big data, i.e., volume, velocity, variety, variability, veracity, visualization, and value. This paper will give an overview of both theories and applications of machine learning methods as applicable to petrophysical big data analysis.\u0000 Recent publications indicate that petrophysical data-driven analytics (PDDA) has been emerging as an active sub-discipline of petrophysics. Field examples from the petrophysics literature will be used to illustrate the advantages of machine learning in the following technical areas: (1) Geological facies classification or petrophysical rock typing; (2) Seismic rock properties or rock physics modeling; (3) Petrophysical/geochemical/geomechanical properties prediction; (3) Fast physical modeling of logging tools; (4) Well and reservoir surveillance; (6) Automated data quality control; (7) Pseudo data generation; and (8) Logging or coring operation guidance.\u0000 The paper will also review the major challenges that need to be overcome before the potentially game-changing value of machine learning for petrophysics discipline can be realized. First, a robust theoretical foundation to support the application of machine leaning to petrophysical interpretation should be established; second, the utility of existing machine learning algorithms must be evaluated and tested in different petrophysical tasks with different data scenarios; third, procedures to control the quality of data used in machine leaning algorithms need to be implemented and the associated uncertainties need to be appropriately addressed. The paper will outlook the future opportunities of enabling advanced data analytics to solve challenging oilfield problems in the era of the 4th industrial revolution (IR4.0).","PeriodicalId":11031,"journal":{"name":"Day 4 Thu, March 21, 2019","volume":"111 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81020156","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}
Hamza Ali, Abdur Rahman Shah, A. H. Akram, W. Khan, F. Siddiqui, Abdul Waheed, Faizan Ahmed
A recent study addressed the modelling challenges of Alpha* gas condensate field. Alpha gas condensate field has a gas in-place of about 1 TCF, and both condensate and black oil production in addition. The field has been producing from two reservoirs S-I and D-I, for the past 26 years. Alpha field is sub-divided into two segments called the Central Area and the Northern Area which are separated by a fault as shown in Figure 2. * Not its real name.
{"title":"Modelling Complex Fluid Production Behaviour in a Gas Condensate Field: A Case Study","authors":"Hamza Ali, Abdur Rahman Shah, A. H. Akram, W. Khan, F. Siddiqui, Abdul Waheed, Faizan Ahmed","doi":"10.2118/194771-MS","DOIUrl":"https://doi.org/10.2118/194771-MS","url":null,"abstract":"\u0000 A recent study addressed the modelling challenges of Alpha* gas condensate field. Alpha gas condensate field has a gas in-place of about 1 TCF, and both condensate and black oil production in addition. The field has been producing from two reservoirs S-I and D-I, for the past 26 years. Alpha field is sub-divided into two segments called the Central Area and the Northern Area which are separated by a fault as shown in Figure 2.\u0000 * Not its real name.","PeriodicalId":11031,"journal":{"name":"Day 4 Thu, March 21, 2019","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90214545","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}
Nassir A. Abalkhail, P. J. Liyanage, Karsinghe A. N. Upamali, G. Pope, K. Mohanty
The goal of this work was to develop a highly efficient alkaline-surfactant-polymer (ASP) process applicable to a high temperature (~100 °C), high salinity (~60,000 ppm) giant carbonate reservoir with very low surfactant retention, an essential requirement for low chemical cost. Phase behavior tests were conducted with anionic surfactants, alkali, co-solvents, brine, and crude oil to identify chemical formulations with ultra-low IFT under reservoir conditions. Corefloods were first conducted in outcrop carbonate cores and then in reservoir cores. The effluent was analyzed for oil, surfactant, pH, salinity and viscosity. Pressure drop was monitored across 4 sections of the core to monitor front propagation. Surfactant adsorption on carbonate surfaces decreases at high pH. The conventional alkali used for ASP floods of sandstones is sodium carbonate. However, sodium carbonate cannot be used in formations containing anhydrite, which is the case for the target reservoir. For this reason, ammonia, sodium hydroxide and a new organic alkali were studied for this application. Ultralow IFT (~0.001 dynes/cm) was achieved with several ASP formulations using the reservoir oil. Coreflood experiments using both outcrop limestone and carbonate reservoir core were conducted using these alkalis. The coreflood results showed good oil recovery and low surfactant retention.
{"title":"ASP Flood Application for a High-Temperature, High-Salinity Carbonate Reservoir","authors":"Nassir A. Abalkhail, P. J. Liyanage, Karsinghe A. N. Upamali, G. Pope, K. Mohanty","doi":"10.2118/194948-MS","DOIUrl":"https://doi.org/10.2118/194948-MS","url":null,"abstract":"\u0000 The goal of this work was to develop a highly efficient alkaline-surfactant-polymer (ASP) process applicable to a high temperature (~100 °C), high salinity (~60,000 ppm) giant carbonate reservoir with very low surfactant retention, an essential requirement for low chemical cost. Phase behavior tests were conducted with anionic surfactants, alkali, co-solvents, brine, and crude oil to identify chemical formulations with ultra-low IFT under reservoir conditions. Corefloods were first conducted in outcrop carbonate cores and then in reservoir cores. The effluent was analyzed for oil, surfactant, pH, salinity and viscosity. Pressure drop was monitored across 4 sections of the core to monitor front propagation. Surfactant adsorption on carbonate surfaces decreases at high pH. The conventional alkali used for ASP floods of sandstones is sodium carbonate. However, sodium carbonate cannot be used in formations containing anhydrite, which is the case for the target reservoir. For this reason, ammonia, sodium hydroxide and a new organic alkali were studied for this application. Ultralow IFT (~0.001 dynes/cm) was achieved with several ASP formulations using the reservoir oil. Coreflood experiments using both outcrop limestone and carbonate reservoir core were conducted using these alkalis. The coreflood results showed good oil recovery and low surfactant retention.","PeriodicalId":11031,"journal":{"name":"Day 4 Thu, March 21, 2019","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88423441","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}
Honglin Shu, Gaocheng Wang, Yuan Xiaojun, Yin Kaigui, Qin-Fei Li, Luo Yufeng, Da-li Wang
The identification of the shale gas target in the Longmaxi shale horizontal wells presents challenges due to the similar gamma ray readings of neighboring layers and the complex structural faults that seismic data cannot detect because of resolution limitations. This makes the correlations of the shale gas log evaluation and actual gas production between horizontal laterals difficult. The average thickness of the shale gas target in vertical offset wells and pilot wells is about 5 m. The well trajectories of the horizontal wells in the Longmaxi shale were planned from the gamma ray logs of vertical offset wells and seismic data, and the placement of the horizontal wells was performed by a gamma ray log measurement-while-drilling tool. Identifying the shale gas target layer and optimum stimulation staging design in two oil-based mud horizontal wells were the objectives in this case study. The lengths of the horizontal wells were 1821 m and 1300 m. The similar gamma ray readings were in the Lower Silurian shale gas target layer and the Upper Ordovicican Wufeng shale layer. Between the layers was the widespread shell limestone of the top of the Upper Wufeng Formation, from 0.2 m to 0.6 m thick in the field studied. The similar gamma ray readings indicated multiple possibilities for the shale gas target along the horizontal wellbores, among which were portions that were out of the shale gas target zone and portions that were inside the shale gas target zone. In addition, the structural faults that cut through the horizontal wells made discriminating among the multiple possibilities more complex. New-generation high-definition oil-based microresistivity image logs were run in the two oil-based mud horizontal wells. The objective was originally to identify natural fractures, which information was commonly used in perforation cluster design and stimulation staging. However, the high-definition oil-based microresistivity image logs provided more detailed structural information along the horizontal wellbores, including displacement faults and structural dips. With the help of 3D structural modeling techniques, the true stratigraphic drilling polarity and structural model of the horizontal wells revealed the position of the horizontal wellbores relative to the shale gas target layer. The portions inside and outside the shale gas target zone were identified from the structural model. The new-generation high-definition oil-based microresistivity image logging was a good solution for the identification of the shale gas target in the Longmaxi shale horizontal wells. It eliminates the multiple possibilities of the shale gas target from gamma ray logs along the horizontal wellbores. The more detailed structural information about fractures, faults, and the portions inside the shale gas target zone was used in optimum stimulation staging design. In addition, the oil-based microresistivity image logs were used to distinguish between open fracture and cemented
龙马溪页岩水平井邻近层的伽马射线读数相似，且构造断层复杂，地震数据受分辨率限制无法探测，这给页岩气靶区识别带来了挑战。这使得页岩气测井评价与水平分支间实际产气量的相关性变得困难。垂直邻井和先导井的页岩气靶层平均厚度约为5 m。根据垂直邻井的伽马测井资料和地震资料，规划了龙马溪页岩水平井的井眼轨迹，并利用随钻伽马测井工具进行了水平井的布置。本案例研究的目的是确定两口油基泥浆水平井的页岩气目标层和优化增产阶段设计。水平井长度分别为1821 m和1300 m。下志留统页岩气目标层和上奥陶统五峰页岩层的伽玛射线读数相似。层与层之间为广泛分布的上五峰组顶部壳灰岩，现场研究厚度为0.2 m ~ 0.6 m。相似的伽马射线读数表明，页岩气目标沿水平井方向存在多种可能性，其中部分在页岩气目标带外，部分在页岩气目标带内。此外，横断水平井的构造断层使多种可能性的判别变得更加复杂。在两口油基泥浆水平井中进行了新一代高清油基微电阻率成像测井。最初的目标是识别天然裂缝，这些信息通常用于射孔簇设计和增产阶段。然而，高分辨率油基微电阻率成像测井可以提供更详细的水平井筒结构信息，包括位移断层和构造倾角。借助三维结构建模技术，获得了水平井的真实地层钻井极性和结构模型，揭示了水平井相对于页岩气目标层的位置。根据构造模型，确定了页岩气靶区内和区外的部分。新一代高清油基微电阻率成像测井是龙马溪页岩水平井页岩气目标识别的良好解决方案。它消除了沿水平井的伽马射线测井中页岩气目标的多种可能性。更详细的裂缝、断层和页岩气目标区内部的结构信息被用于优化增产阶段设计。此外，利用油基微电阻率成像测井资料进行反演处理，区分开缝和胶结裂缝。
{"title":"Identification of Shale Gas Target by High Definition Oil Based Microresistivity Image Logs in Horizontal Longmaxi Shale Wells","authors":"Honglin Shu, Gaocheng Wang, Yuan Xiaojun, Yin Kaigui, Qin-Fei Li, Luo Yufeng, Da-li Wang","doi":"10.2118/194836-MS","DOIUrl":"https://doi.org/10.2118/194836-MS","url":null,"abstract":"\u0000 The identification of the shale gas target in the Longmaxi shale horizontal wells presents challenges due to the similar gamma ray readings of neighboring layers and the complex structural faults that seismic data cannot detect because of resolution limitations. This makes the correlations of the shale gas log evaluation and actual gas production between horizontal laterals difficult. The average thickness of the shale gas target in vertical offset wells and pilot wells is about 5 m. The well trajectories of the horizontal wells in the Longmaxi shale were planned from the gamma ray logs of vertical offset wells and seismic data, and the placement of the horizontal wells was performed by a gamma ray log measurement-while-drilling tool.\u0000 Identifying the shale gas target layer and optimum stimulation staging design in two oil-based mud horizontal wells were the objectives in this case study. The lengths of the horizontal wells were 1821 m and 1300 m. The similar gamma ray readings were in the Lower Silurian shale gas target layer and the Upper Ordovicican Wufeng shale layer. Between the layers was the widespread shell limestone of the top of the Upper Wufeng Formation, from 0.2 m to 0.6 m thick in the field studied. The similar gamma ray readings indicated multiple possibilities for the shale gas target along the horizontal wellbores, among which were portions that were out of the shale gas target zone and portions that were inside the shale gas target zone. In addition, the structural faults that cut through the horizontal wells made discriminating among the multiple possibilities more complex.\u0000 New-generation high-definition oil-based microresistivity image logs were run in the two oil-based mud horizontal wells. The objective was originally to identify natural fractures, which information was commonly used in perforation cluster design and stimulation staging. However, the high-definition oil-based microresistivity image logs provided more detailed structural information along the horizontal wellbores, including displacement faults and structural dips. With the help of 3D structural modeling techniques, the true stratigraphic drilling polarity and structural model of the horizontal wells revealed the position of the horizontal wellbores relative to the shale gas target layer. The portions inside and outside the shale gas target zone were identified from the structural model.\u0000 The new-generation high-definition oil-based microresistivity image logging was a good solution for the identification of the shale gas target in the Longmaxi shale horizontal wells. It eliminates the multiple possibilities of the shale gas target from gamma ray logs along the horizontal wellbores. The more detailed structural information about fractures, faults, and the portions inside the shale gas target zone was used in optimum stimulation staging design. In addition, the oil-based microresistivity image logs were used to distinguish between open fracture and cemented ","PeriodicalId":11031,"journal":{"name":"Day 4 Thu, March 21, 2019","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84026588","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}
Maher Rahayyem, P. Mostaghimi, Yara A. Alzahid, Amalia Halim, Lucas Evangelista, R. Armstrong
Enzyme Enhanced Oil Recovery (EEOR) has recently been categorized as one of the most effective EOR mechanisms. Laboratory and field studies have reported up to 16% of incremental oil recovery rates. EEOR recovers oil mainly by two main mechanisms: lowering the interfacial tension between brine and oil and altering the wettability on rock grains to a more water-wet condition. Therefore, EEOR would promote mobilization of capillary-trapped oil after regular waterflooding. Since capillary-trapped oil resides at the micro-scale, it is essential to assess EEOR fluid-fluid interaction at that scale. To further investigate the ways in which these enzymes contribute to EOR, an experimental micro-scale approach was developed in which EEOR was analyzed using polydimethylsiloxane (PDMS) microfluidic devices. The PDMS microfluidics device was based on X-ray micro-CT images of a Bentheimer sandstone with resolution of 4.95 μm. We first compared the IFT reduction capabilities of one class of enzyme (Apollo GreenZyme ®) and a commercial surfactant (J13131) obtained from Shell Chemicals. For GreenZyme concentrations of 0.5, 1.5 and 2 wt%, the IFT values between GreenZyme solution and oil are 4.2, 0.7 and 0.6 mN/m, respectively. Whereas the IFT values for 0.5 wt% surfactant solutions and deionized water are 1.1 and 32 mN/m, respectively. We then compared the oil recovery of the two systems using the aforementioned sandstone PDMS microfluidics device. Recovery values up to 92% of oilwere obtained using GreenZyme. Surfactant and waterflooding on the same PDMS chips had recovery values of 86 and 80%, respectively. This study provides insights and direct visualization of the micro-scale oil recovery mechanisms of EEOR that can be used for design of effective EEOR flooding.
{"title":"Enzyme Enhanced Oil Recovery EEOR: A Microfluidics Approach","authors":"Maher Rahayyem, P. Mostaghimi, Yara A. Alzahid, Amalia Halim, Lucas Evangelista, R. Armstrong","doi":"10.2118/195116-MS","DOIUrl":"https://doi.org/10.2118/195116-MS","url":null,"abstract":"\u0000 Enzyme Enhanced Oil Recovery (EEOR) has recently been categorized as one of the most effective EOR mechanisms. Laboratory and field studies have reported up to 16% of incremental oil recovery rates. EEOR recovers oil mainly by two main mechanisms: lowering the interfacial tension between brine and oil and altering the wettability on rock grains to a more water-wet condition. Therefore, EEOR would promote mobilization of capillary-trapped oil after regular waterflooding. Since capillary-trapped oil resides at the micro-scale, it is essential to assess EEOR fluid-fluid interaction at that scale. To further investigate the ways in which these enzymes contribute to EOR, an experimental micro-scale approach was developed in which EEOR was analyzed using polydimethylsiloxane (PDMS) microfluidic devices. The PDMS microfluidics device was based on X-ray micro-CT images of a Bentheimer sandstone with resolution of 4.95 μm. We first compared the IFT reduction capabilities of one class of enzyme (Apollo GreenZyme ®) and a commercial surfactant (J13131) obtained from Shell Chemicals. For GreenZyme concentrations of 0.5, 1.5 and 2 wt%, the IFT values between GreenZyme solution and oil are 4.2, 0.7 and 0.6 mN/m, respectively. Whereas the IFT values for 0.5 wt% surfactant solutions and deionized water are 1.1 and 32 mN/m, respectively. We then compared the oil recovery of the two systems using the aforementioned sandstone PDMS microfluidics device. Recovery values up to 92% of oilwere obtained using GreenZyme. Surfactant and waterflooding on the same PDMS chips had recovery values of 86 and 80%, respectively. This study provides insights and direct visualization of the micro-scale oil recovery mechanisms of EEOR that can be used for design of effective EEOR flooding.","PeriodicalId":11031,"journal":{"name":"Day 4 Thu, March 21, 2019","volume":"172 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77344140","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}
Zhuoran Li, Tianluo Chen, Yang Ning, Kaiyi Zhang, G. Qin
Shale formations exhibit multi-scale geological features such as nanopores in formation matrix and fractures at multiple length scales. Accurate prediction of relative permeability and capillary pressure are vital in numerical simulations of shale reservoirs. The multi-scale geological features of shale formations present great challenges for traditional experimental approach. Compared to nanopores in formation matrix, fractures, especially connected fractures, have much more significant impact on multiphase flows. Traditional flow models like Darcy's law are not valid for modeling fluid flow in fracture space nor in nanopores. In this work, we apply multiphase lattice Boltzmann simulation for unsteady-state waterflooding process in highly fractured samples to study the effects of fracture connectivity, wetting preference, and gravitional forces.
{"title":"Numerical Simulation of Waterflooding Process using Lattice Boltzmann Method to Estimate Relative Permeability for Fractured Unconventional Reservoirs","authors":"Zhuoran Li, Tianluo Chen, Yang Ning, Kaiyi Zhang, G. Qin","doi":"10.2118/194770-MS","DOIUrl":"https://doi.org/10.2118/194770-MS","url":null,"abstract":"\u0000 Shale formations exhibit multi-scale geological features such as nanopores in formation matrix and fractures at multiple length scales. Accurate prediction of relative permeability and capillary pressure are vital in numerical simulations of shale reservoirs. The multi-scale geological features of shale formations present great challenges for traditional experimental approach. Compared to nanopores in formation matrix, fractures, especially connected fractures, have much more significant impact on multiphase flows. Traditional flow models like Darcy's law are not valid for modeling fluid flow in fracture space nor in nanopores. In this work, we apply multiphase lattice Boltzmann simulation for unsteady-state waterflooding process in highly fractured samples to study the effects of fracture connectivity, wetting preference, and gravitional forces.","PeriodicalId":11031,"journal":{"name":"Day 4 Thu, March 21, 2019","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74838729","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}
S. Hadidi, Hilal Yaarubi, U. Baaske, Sakharin Suwannathatsa, S. Farsi, L. Bazalgette, L. Hamdoun
The infill potential of one of the most complex fractured carbonate reservoirs in the Sultanate of Oman has been evaluated through the integration, visualization and analysis of different data sources. The field has been split into different simplified genetic geobodies which contain the culmination of facies changes that define rock quality, fluid fill, oil saturation distribution and fracture network, amongst other properties that affect fluid flow. The long production history of more than 45 years, along with the large number of logged long horizontal wells scattered around the field, were key enabler for the analytical methodology. Production data, coupled with resistivity logs in horizontal wells, viewed through time were the backbone of the analysis. Data analysis was achieved by combining these data in a single platform and performing the analysis at different slices of time. At each timeslice, different interpretations were inferred that explain the observed production behaviour and remaining oil saturation from the logged wells. The interpretations were narrowed down into a minimum number of realizations by combining interpretations from the same area gathered from different slices of time. The analysis has resulted in the identification of four genetic performance regions in the field. Each region approximates a primary depositional facies belt and has a general defined relative behaviour of initial wells potential, water-cut development, initial and remaining oil saturation and, most importantly, infill wells potential. The analysis has aided in narrowing the subsurface uncertainties and has given a promising explanation for the large variations in wells behaviour. Infill wells opportunities have been identified, selected and ranked relatively in each of the regions. The value of data analytics on large volumes of acquired information normally not used was demonstrated. Visualization of different data sources in one platform is a challenging task that usually stops engineers from experimenting. The team has found fit for purpose solutions to visualize different data sources through time. The shift of mind-set from uncertain complex models and evaluations into finding simple genetic performance regions of the reservoir was vital in unravelling infill potential.
{"title":"Beyond One-Dimensional Evaluations: The Search for Genetic Reservoir Regions through Time & Space","authors":"S. Hadidi, Hilal Yaarubi, U. Baaske, Sakharin Suwannathatsa, S. Farsi, L. Bazalgette, L. Hamdoun","doi":"10.2118/194844-MS","DOIUrl":"https://doi.org/10.2118/194844-MS","url":null,"abstract":"\u0000 The infill potential of one of the most complex fractured carbonate reservoirs in the Sultanate of Oman has been evaluated through the integration, visualization and analysis of different data sources. The field has been split into different simplified genetic geobodies which contain the culmination of facies changes that define rock quality, fluid fill, oil saturation distribution and fracture network, amongst other properties that affect fluid flow. The long production history of more than 45 years, along with the large number of logged long horizontal wells scattered around the field, were key enabler for the analytical methodology.\u0000 Production data, coupled with resistivity logs in horizontal wells, viewed through time were the backbone of the analysis. Data analysis was achieved by combining these data in a single platform and performing the analysis at different slices of time. At each timeslice, different interpretations were inferred that explain the observed production behaviour and remaining oil saturation from the logged wells. The interpretations were narrowed down into a minimum number of realizations by combining interpretations from the same area gathered from different slices of time.\u0000 The analysis has resulted in the identification of four genetic performance regions in the field. Each region approximates a primary depositional facies belt and has a general defined relative behaviour of initial wells potential, water-cut development, initial and remaining oil saturation and, most importantly, infill wells potential. The analysis has aided in narrowing the subsurface uncertainties and has given a promising explanation for the large variations in wells behaviour. Infill wells opportunities have been identified, selected and ranked relatively in each of the regions.\u0000 The value of data analytics on large volumes of acquired information normally not used was demonstrated. Visualization of different data sources in one platform is a challenging task that usually stops engineers from experimenting. The team has found fit for purpose solutions to visualize different data sources through time. The shift of mind-set from uncertain complex models and evaluations into finding simple genetic performance regions of the reservoir was vital in unravelling infill potential.","PeriodicalId":11031,"journal":{"name":"Day 4 Thu, March 21, 2019","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83187946","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}
Mohammed Murif Al Rubaii, Abdullah Yami, Eno Omini
Utilization of drilled wells operations’ records is required to perform improvement of performance to minimize drilling cost of planned drilling of new and re-entry wells (workover – wells). Many operators are always interested in finding optimum ways to save on drilling cost. Optimization of Rate of Penetration (ROP) has direct effects on cost reduction. Different Techniques were used to optimize ROP such as regression technique, multiple linear regression technique, neural network, artificial neural network methods, and basic reference of Bayesian networks. There are several factors that will limit application of ROP optimization models in different hole sections with high degree of accuracy. It is the authors’ opinion that modeling on smaller selected section with controlled parameters will give better optimization and validation. In this paper an empirical correlation of rate of penetration (ROP) is presented for a particular hole section. The data selected are from same hole size, formation type and mud type. It is based on monitoring and controlling simultaneously the applied weight on bit (WOB), drill-string's rotation (RPM), Torque (TRQ) and rig pump's flow rate (GPM). During this study will demonstrate the use of this empirical correlation to improve drilling in a hole section by more than 50%. The developed model also has high potential to be automated in real time operating environment to improve drilling performance.
{"title":"A Robust Correlation Improves Well Drilling Performance","authors":"Mohammed Murif Al Rubaii, Abdullah Yami, Eno Omini","doi":"10.2118/195062-MS","DOIUrl":"https://doi.org/10.2118/195062-MS","url":null,"abstract":"\u0000 Utilization of drilled wells operations’ records is required to perform improvement of performance to minimize drilling cost of planned drilling of new and re-entry wells (workover – wells). Many operators are always interested in finding optimum ways to save on drilling cost. Optimization of Rate of Penetration (ROP) has direct effects on cost reduction. Different Techniques were used to optimize ROP such as regression technique, multiple linear regression technique, neural network, artificial neural network methods, and basic reference of Bayesian networks. There are several factors that will limit application of ROP optimization models in different hole sections with high degree of accuracy. It is the authors’ opinion that modeling on smaller selected section with controlled parameters will give better optimization and validation. In this paper an empirical correlation of rate of penetration (ROP) is presented for a particular hole section. The data selected are from same hole size, formation type and mud type. It is based on monitoring and controlling simultaneously the applied weight on bit (WOB), drill-string's rotation (RPM), Torque (TRQ) and rig pump's flow rate (GPM). During this study will demonstrate the use of this empirical correlation to improve drilling in a hole section by more than 50%. The developed model also has high potential to be automated in real time operating environment to improve drilling performance.","PeriodicalId":11031,"journal":{"name":"Day 4 Thu, March 21, 2019","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90875820","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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