Analogue outcrops can be used to prepare geoscientists with realistic expectations and responses for Geosteering ultra-long horizontal wells (ERD) in thin reservoirs with different scales of faults, and uncertainty in fault zone parameters and characteristics. Geosteering ultra-long horizontal wells in specific, thin, meter-thick target zones within reservoirs is challenged when sub-seismic faults are present or where seismic scale fault throw and fault location is ill-defined or imprecisely known. This paper defines the challenge of how analogue outcrops can be used to prepare geoscientists with realistic expectations and responses to such operational difficulties in faulted carbonates, irrespective of the tools employed to characterize encountered faults. Geosteering wells in reservoirs with different scales of faults and uncertainty in fault zone character and detection limits can lead to: (i) extensive ‘out of zone’ intervals and (ii) undulating wellbores (when attempting to retrieve target layer positioning), whereby well productivity and accessibility are compromised. Using faulted carbonate field analogues can direct the operation geologist's geosteering response to such faulted scenarios. Descriptions from outcrops are used to address subsurface scenarios of marker horizon(s) and their lateral/spatial variability; diagenesis related to faults at outcrop and expected variations along wellbore laterals in the oilfield. Additionally, offsets/throws, damage zone geometries for thin-bed reservoir understanding of fault zone effects in low-offset structures. Appreciation of faults in outcrops allows an understanding of expectations whilst drilling according to the following: (1) Scales of features from seismic to sub-seismic damage zones: what to expect when geosteering within / out of zone, across faults with indeterminate throws. (2) Understandings from 3D analogues/geometries applied predictively to field development, targeting specific thin reservoir zones / key marker beds. Several oil- well case-examples highlight the response in steering wellbores located within specific thin target zones whereby faults were expected, but where fault throw differed significantly to what was anticipated from initial seismic interpretation. Examples elucidating the application include a meter-thick dolomite zone within a very thick limestone reservoir where injector and producer wells are completed, where the wellbore remains within reservoir but out of specific target zone (how to marry smooth wellbore with layer conformance). Furthermore, for very thin reservoirs primarily located within non-reservoir carbonates, minor faults would misdirect wellbore into argillaceous limestone above or below the reservoirs. Faulted zones with water influx mapped from LWD where modelled property responses can be better characterized by low-offset faults with compartmentalizing effects for completion strategies. Even with an extensive suite of logs to characterize f
{"title":"Capturing Fault Effects in Thin Reservoirs for Geosteering Improvements in Developing Offshore Carbonate Fields","authors":"E. A. Mohamed, H. E. Edwards","doi":"10.2118/208160-ms","DOIUrl":"https://doi.org/10.2118/208160-ms","url":null,"abstract":"Analogue outcrops can be used to prepare geoscientists with realistic expectations and responses for Geosteering ultra-long horizontal wells (ERD) in thin reservoirs with different scales of faults, and uncertainty in fault zone parameters and characteristics. Geosteering ultra-long horizontal wells in specific, thin, meter-thick target zones within reservoirs is challenged when sub-seismic faults are present or where seismic scale fault throw and fault location is ill-defined or imprecisely known. This paper defines the challenge of how analogue outcrops can be used to prepare geoscientists with realistic expectations and responses to such operational difficulties in faulted carbonates, irrespective of the tools employed to characterize encountered faults. Geosteering wells in reservoirs with different scales of faults and uncertainty in fault zone character and detection limits can lead to: (i) extensive ‘out of zone’ intervals and (ii) undulating wellbores (when attempting to retrieve target layer positioning), whereby well productivity and accessibility are compromised. Using faulted carbonate field analogues can direct the operation geologist's geosteering response to such faulted scenarios. Descriptions from outcrops are used to address subsurface scenarios of marker horizon(s) and their lateral/spatial variability; diagenesis related to faults at outcrop and expected variations along wellbore laterals in the oilfield. Additionally, offsets/throws, damage zone geometries for thin-bed reservoir understanding of fault zone effects in low-offset structures. Appreciation of faults in outcrops allows an understanding of expectations whilst drilling according to the following: (1) Scales of features from seismic to sub-seismic damage zones: what to expect when geosteering within / out of zone, across faults with indeterminate throws. (2) Understandings from 3D analogues/geometries applied predictively to field development, targeting specific thin reservoir zones / key marker beds. Several oil- well case-examples highlight the response in steering wellbores located within specific thin target zones whereby faults were expected, but where fault throw differed significantly to what was anticipated from initial seismic interpretation. Examples elucidating the application include a meter-thick dolomite zone within a very thick limestone reservoir where injector and producer wells are completed, where the wellbore remains within reservoir but out of specific target zone (how to marry smooth wellbore with layer conformance). Furthermore, for very thin reservoirs primarily located within non-reservoir carbonates, minor faults would misdirect wellbore into argillaceous limestone above or below the reservoirs. Faulted zones with water influx mapped from LWD where modelled property responses can be better characterized by low-offset faults with compartmentalizing effects for completion strategies. Even with an extensive suite of logs to characterize f","PeriodicalId":10967,"journal":{"name":"Day 1 Mon, November 15, 2021","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90943341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Production prediction continues to play an increasingly significant role in reservoir development adjustment and optimization, especially in water-alternating-gas (WAG) flooding. As artificial intelligence continues to develop, data-driven machine learning method can establish a robust model based on massive data to clarify development risks and challenges, predict development dynamic characteristics in advance. This study gathers over 15 years actual data from targeted carbonate reservoir and establishes a robust Long Short-Term Memory (LSTM) neural network prediction model based on correlation analysis, data cleaning, feature variables selection, hyper-parameters optimization and model evaluation to forecast oil production, gas-oil ratio (GOR), and water cut (WC) of WAG flooding. In comparison to traditional reservoir numerical simulation (RNS), LSTM neural networks have a huge advantage in terms of computational efficiency and prediction accuracy. The calculation time of LSTM method is 864% less than reservoir numerical simulation method, while prediction error of LSTM method is 261% less than RNS method. We classify producers into three types based on the prediction results and propose optimization measures aimed at the risks and challenges they faced. Field implementation indicates promising outcome with better reservoir support, lower GOR, lower WC, and stabler oil production. This study provides a novel direction for application of artificial intelligence in WAG flooding development and optimization.
{"title":"Prediction and Optimization of WAG Flooding by Using LSTM Neural Network Model in Middle East Carbonate Reservoir","authors":"Ruijie Huang, Chenji Wei, Baozhu Li, Jian Yang, Suwei Wu, Xin Xu, Yajie Ou, L. Xiong, Yuankeli Lou, Zhengzhong Li, Ya Deng, Chenjun Zhang","doi":"10.2118/207584-ms","DOIUrl":"https://doi.org/10.2118/207584-ms","url":null,"abstract":"\u0000 Production prediction continues to play an increasingly significant role in reservoir development adjustment and optimization, especially in water-alternating-gas (WAG) flooding. As artificial intelligence continues to develop, data-driven machine learning method can establish a robust model based on massive data to clarify development risks and challenges, predict development dynamic characteristics in advance. This study gathers over 15 years actual data from targeted carbonate reservoir and establishes a robust Long Short-Term Memory (LSTM) neural network prediction model based on correlation analysis, data cleaning, feature variables selection, hyper-parameters optimization and model evaluation to forecast oil production, gas-oil ratio (GOR), and water cut (WC) of WAG flooding. In comparison to traditional reservoir numerical simulation (RNS), LSTM neural networks have a huge advantage in terms of computational efficiency and prediction accuracy. The calculation time of LSTM method is 864% less than reservoir numerical simulation method, while prediction error of LSTM method is 261% less than RNS method. We classify producers into three types based on the prediction results and propose optimization measures aimed at the risks and challenges they faced. Field implementation indicates promising outcome with better reservoir support, lower GOR, lower WC, and stabler oil production. This study provides a novel direction for application of artificial intelligence in WAG flooding development and optimization.","PeriodicalId":10967,"journal":{"name":"Day 1 Mon, November 15, 2021","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81443697","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}
M. T. Al-Murayri, A. Hassan, D. Alrukaibi, A. Al-Qenae, Jimmy Nesbit, Philippe Al Khoury, B. Thery, A. Zaitoun, G. Omonte, N. Salehi, M. Pitts, K. Wyatt, E. Dean
Mature carbonate reservoirs under waterflood in Kuwait suffer from relatively low oil recovery due to poor sweep efficiency, both areal and microscopic. An Alkaline-Surfactant-Polymer (ASP) pilot is in progress targeting the Sabriyah Mauddud (SAMA) reservoir in pursuit of reserves growth and production sustainability. SAMA suffers from reservoir heterogeneities mainly associated with permeability contrast which may be improved with a conformance treatment to de-risk pre-mature breakthrough of water and chemical EOR agents in preparation for subsequent ASP injection and to improve reservoir contact by the injected fluids. Design of the gel conformance treatment was multi-faceted. Rapid breakthrough of tracers at the pilot producer from each of the individual injectors, less than 3 days, implied a direct connection from the injectors to the producer and poses significant risk to the success of the pilot. A dynamic model of the SAMA pilot was used to estimate in the potential injection of either a high viscous polymer solution (~200 cp) or a gel conformance treatment to improve contact efficiency, diverting injected fluid into oil saturated reservoir matrix. High viscosity polymer injection scenarios were simulated in the extracted subsector model and showed little to no effect on diverting fluids from the high permeability streak into the matrix. Gel conformance treatment, however, provides benefit to the SAMA pilot with important limitations. Gel treatment diverts injected fluid from the high permeability zone into lower permeability, higher oil saturated reservoir. After a gel treatment, the ASP increases the oil cut from 3% to 75% while increasing the cumulative oil recovery by more than 50 MSTB oil over ASP following a high viscosity polymer slug alone. Laboratory design of the gel conformance system for the SAMA ASP pilot involved blending of two polymer types (AN 125SH, an ATBS type polymer, and P320 VLM and P330, synthetic copolymers) and two crosslinkers (chromium acetate and X1050, an organic crosslinker). Bulk testing with the polymer-crosslinker combinations indicated that SAMA reservoir brine resulted in not gel system that would work in the SAMA reservoir, resulting in the recommendation of using 2% KCl in treated water for gel formulation. AN 125 SH with S1050 produce good gels but with short gelation times and AS 125 SH with chromium acetate developed low gels consistency in both waters. P330 and P320 VLM gave good gels with slow gelation times with X1050 crosslinker in 2% KCl. Corefloods with the P330-X 1050 showed good injectivity and ultimately a reduction of permeability of about 200-fold. A P330-X 1050 was recommended for numerical simulation studies. Numerical simulator was calibrated by matching bulk gel viscosity increases and coreflood permeability changes. Numerical simulation indicated two of the four injection wells (SA-0557 and SA-0559) injection profile will change compared to water. Overall injection rate was reduced
{"title":"Design of in-Depth Conformance Gel Treatment to De-Risk ASP Flooding in a Major Carbonate Reservoir","authors":"M. T. Al-Murayri, A. Hassan, D. Alrukaibi, A. Al-Qenae, Jimmy Nesbit, Philippe Al Khoury, B. Thery, A. Zaitoun, G. Omonte, N. Salehi, M. Pitts, K. Wyatt, E. Dean","doi":"10.2118/208070-ms","DOIUrl":"https://doi.org/10.2118/208070-ms","url":null,"abstract":"\u0000 Mature carbonate reservoirs under waterflood in Kuwait suffer from relatively low oil recovery due to poor sweep efficiency, both areal and microscopic. An Alkaline-Surfactant-Polymer (ASP) pilot is in progress targeting the Sabriyah Mauddud (SAMA) reservoir in pursuit of reserves growth and production sustainability. SAMA suffers from reservoir heterogeneities mainly associated with permeability contrast which may be improved with a conformance treatment to de-risk pre-mature breakthrough of water and chemical EOR agents in preparation for subsequent ASP injection and to improve reservoir contact by the injected fluids. Design of the gel conformance treatment was multi-faceted. Rapid breakthrough of tracers at the pilot producer from each of the individual injectors, less than 3 days, implied a direct connection from the injectors to the producer and poses significant risk to the success of the pilot. A dynamic model of the SAMA pilot was used to estimate in the potential injection of either a high viscous polymer solution (~200 cp) or a gel conformance treatment to improve contact efficiency, diverting injected fluid into oil saturated reservoir matrix. High viscosity polymer injection scenarios were simulated in the extracted subsector model and showed little to no effect on diverting fluids from the high permeability streak into the matrix. Gel conformance treatment, however, provides benefit to the SAMA pilot with important limitations. Gel treatment diverts injected fluid from the high permeability zone into lower permeability, higher oil saturated reservoir. After a gel treatment, the ASP increases the oil cut from 3% to 75% while increasing the cumulative oil recovery by more than 50 MSTB oil over ASP following a high viscosity polymer slug alone. Laboratory design of the gel conformance system for the SAMA ASP pilot involved blending of two polymer types (AN 125SH, an ATBS type polymer, and P320 VLM and P330, synthetic copolymers) and two crosslinkers (chromium acetate and X1050, an organic crosslinker). Bulk testing with the polymer-crosslinker combinations indicated that SAMA reservoir brine resulted in not gel system that would work in the SAMA reservoir, resulting in the recommendation of using 2% KCl in treated water for gel formulation. AN 125 SH with S1050 produce good gels but with short gelation times and AS 125 SH with chromium acetate developed low gels consistency in both waters. P330 and P320 VLM gave good gels with slow gelation times with X1050 crosslinker in 2% KCl. Corefloods with the P330-X 1050 showed good injectivity and ultimately a reduction of permeability of about 200-fold. A P330-X 1050 was recommended for numerical simulation studies. Numerical simulator was calibrated by matching bulk gel viscosity increases and coreflood permeability changes. Numerical simulation indicated two of the four injection wells (SA-0557 and SA-0559) injection profile will change compared to water. Overall injection rate was reduced","PeriodicalId":10967,"journal":{"name":"Day 1 Mon, November 15, 2021","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80523012","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}
Field operations generate large volumes of data from various equipment and associated Meta data such as inspection due dates, maintenance schedule, people on board, etc. The data is often stored in silos with a data guardian for each entity. The objective of this project was to volarize the data by developing engineered KPI's to drive decision making and make data accessible for everyone in the organization to foster cross collaboration. Data analytics and visualization solutions were developed to automate low value-added tasks either using robotic process automation scripts or business intelligence reporting tool. Data was residing either in spreadsheet or native applications. With support of IT, centralized database was established. Scrum agile project management techniques were used to develop digital solutions. A high-level digital road map was created consulting all teams including stake holders. Use cases were identified and captured in lean A3 problem solving format. Each use case clearly identified the benefits to organization, and this was used to prioritize the use cases. A sprint was set-up with agile team and products were developed as per end user's expectation. The constant feedback loop via daily stand-up meetings helped the team deliver value added products. Digital solutions were developed to automate low value-added tasks so employees can focus on improving systems instead of producing reports. By developing engineering KPI's and predictive analytics, technical authority could shift from reactive maintenance to pro-active maintenance. Using linear regression machine learning, early warning digital solution was developed to monitor and notify technical authority to clean strainers. The production team achieved 0.75 full time equivalent (FTE) in time savings by automating reports. By visualizing operations data such as flaring, production profiles; the team minimized flaring leading to 1% OPEX cost saving. Around 10% of chemical budget was saved by monitoring chemical injections at all platforms. Similar cost savings were achieved by visualizing data for other disciplines such as maintenance and HSE teams. By being better informed about wells annuli pressure build-up via email notifications, wells integrity team reduced the associated risk. By forming a multi-disciplinary agile team with business and delivery team, digital team deployed 20+ digital products over a short time frame of 2 years.
{"title":"Digital Transformation Journey of Field Operations at Abu Dhabi Offshore Field in UAE","authors":"Talha Rafi Ahmed, Bastien Januel, Morealvin Fuenmayor","doi":"10.2118/207386-ms","DOIUrl":"https://doi.org/10.2118/207386-ms","url":null,"abstract":"\u0000 Field operations generate large volumes of data from various equipment and associated Meta data such as inspection due dates, maintenance schedule, people on board, etc. The data is often stored in silos with a data guardian for each entity. The objective of this project was to volarize the data by developing engineered KPI's to drive decision making and make data accessible for everyone in the organization to foster cross collaboration.\u0000 Data analytics and visualization solutions were developed to automate low value-added tasks either using robotic process automation scripts or business intelligence reporting tool. Data was residing either in spreadsheet or native applications. With support of IT, centralized database was established. Scrum agile project management techniques were used to develop digital solutions. A high-level digital road map was created consulting all teams including stake holders. Use cases were identified and captured in lean A3 problem solving format. Each use case clearly identified the benefits to organization, and this was used to prioritize the use cases. A sprint was set-up with agile team and products were developed as per end user's expectation. The constant feedback loop via daily stand-up meetings helped the team deliver value added products.\u0000 Digital solutions were developed to automate low value-added tasks so employees can focus on improving systems instead of producing reports. By developing engineering KPI's and predictive analytics, technical authority could shift from reactive maintenance to pro-active maintenance. Using linear regression machine learning, early warning digital solution was developed to monitor and notify technical authority to clean strainers. The production team achieved 0.75 full time equivalent (FTE) in time savings by automating reports. By visualizing operations data such as flaring, production profiles; the team minimized flaring leading to 1% OPEX cost saving. Around 10% of chemical budget was saved by monitoring chemical injections at all platforms. Similar cost savings were achieved by visualizing data for other disciplines such as maintenance and HSE teams. By being better informed about wells annuli pressure build-up via email notifications, wells integrity team reduced the associated risk. By forming a multi-disciplinary agile team with business and delivery team, digital team deployed 20+ digital products over a short time frame of 2 years.","PeriodicalId":10967,"journal":{"name":"Day 1 Mon, November 15, 2021","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88301366","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}
M. Al Sawafi, Antonio Andrade, Nitish Kumar, Rahul Gala, Eduardo Marín, Sandeep Soni, Conny Velazco
Petroleum Development Oman (PDO) has been a pioneer in improving Well management processes utilizing its valuable human resources, continuous improvement and digitalization. Managing several PCP wells through Exception Based Surveillance (EBS) methodology had already improved PCP surveillance and optimization across assets. The key to trigger EBS was to keep Operating Envelope (OE), Design Limits updated in Well Management Visualization System (WMVS) after every change in operating speed (RPM), workover and new completion. The sustainable solution was required for automatic update of OEs, having well inflow potential and oil gain opportunities available for quicker optimization decisions for further improvements. PDO has completed a project automating PCP well modeling process where models are built and sustained automatically in Well Management System (WMS) for all active PCP wells, with huge impact on day-to-day operational activities. The paper discusses utilization of physics based well models from WMS to automatically update OE, identify oil gain potential daily and enable real time PCP performance visualization in WMVS. The integration of WMS and WMVS was completed to share data between two systems and automatically update well's OE daily. A tuned well model from WMS was utilized to provide well performance data and sensitivity analysis results for various RPMs. Among the various data obtained from WMS, live OE of torque and fluid above pump (FAP) for various speeds, operating limits, design limits, locked in potential (LIP) for optimization and pump upsize were utilized to process PCP well EBS and create live OE visualization. The visualization is created on a torque-speed chart where a live OE and FAP can be observed in provided picture with current RPM and torque with optimum operating condition. The project is completed after conducting successful change management across PDO assets and after thorough analysis of implementation following benefits were observed: 5% net gain of total PCP production is being executed with zero CAPEX using LIP reports. 50% of engineer's time was saved by updating OEs in WMVS automatically, reduction of false EBS and EBS rationalization. 200% improvement in PCP well performance diagnostics capabilities of Engineers. 15% CAPEX free optimization and pump upsize cases were identified based on well inflow potential. 100% visibility to PCP well's performance was achieved using well model. The visualization has supported engineers monitoring well performance in real time and easily identifying ongoing changes in well and pump performance. PCP well models have supported engineers in new PCP well design and pump upsize. The current efforts in utilizing real time well models, inferred production, automating processes to update OE is one more step toward Digitalization of PCP Surveillance and optimization and to achieve self well optimization for further improving operational efficiency.
{"title":"Intelligent Operating Envelope Integrated with Automated Well Models Improves Asset Wide PCP Surveillance and Optimization","authors":"M. Al Sawafi, Antonio Andrade, Nitish Kumar, Rahul Gala, Eduardo Marín, Sandeep Soni, Conny Velazco","doi":"10.2118/207290-ms","DOIUrl":"https://doi.org/10.2118/207290-ms","url":null,"abstract":"\u0000 Petroleum Development Oman (PDO) has been a pioneer in improving Well management processes utilizing its valuable human resources, continuous improvement and digitalization. Managing several PCP wells through Exception Based Surveillance (EBS) methodology had already improved PCP surveillance and optimization across assets. The key to trigger EBS was to keep Operating Envelope (OE), Design Limits updated in Well Management Visualization System (WMVS) after every change in operating speed (RPM), workover and new completion. The sustainable solution was required for automatic update of OEs, having well inflow potential and oil gain opportunities available for quicker optimization decisions for further improvements.\u0000 PDO has completed a project automating PCP well modeling process where models are built and sustained automatically in Well Management System (WMS) for all active PCP wells, with huge impact on day-to-day operational activities. The paper discusses utilization of physics based well models from WMS to automatically update OE, identify oil gain potential daily and enable real time PCP performance visualization in WMVS.\u0000 The integration of WMS and WMVS was completed to share data between two systems and automatically update well's OE daily. A tuned well model from WMS was utilized to provide well performance data and sensitivity analysis results for various RPMs. Among the various data obtained from WMS, live OE of torque and fluid above pump (FAP) for various speeds, operating limits, design limits, locked in potential (LIP) for optimization and pump upsize were utilized to process PCP well EBS and create live OE visualization. The visualization is created on a torque-speed chart where a live OE and FAP can be observed in provided picture with current RPM and torque with optimum operating condition.\u0000 The project is completed after conducting successful change management across PDO assets and after thorough analysis of implementation following benefits were observed:\u0000 5% net gain of total PCP production is being executed with zero CAPEX using LIP reports. 50% of engineer's time was saved by updating OEs in WMVS automatically, reduction of false EBS and EBS rationalization. 200% improvement in PCP well performance diagnostics capabilities of Engineers. 15% CAPEX free optimization and pump upsize cases were identified based on well inflow potential. 100% visibility to PCP well's performance was achieved using well model.\u0000 The visualization has supported engineers monitoring well performance in real time and easily identifying ongoing changes in well and pump performance. PCP well models have supported engineers in new PCP well design and pump upsize.\u0000 The current efforts in utilizing real time well models, inferred production, automating processes to update OE is one more step toward Digitalization of PCP Surveillance and optimization and to achieve self well optimization for further improving operational efficiency.","PeriodicalId":10967,"journal":{"name":"Day 1 Mon, November 15, 2021","volume":"143 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88554370","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}
In this paper, we will show that it is highly beneficial to model dual-porosity reservoirs using matrix refinement (similar to the multiple interacting continua, MINC, of Preuss, 1985) for water displacing oil. Two practical situations are considered. The first is the effect of matrix refinement on the unsteady-state pressure solution, and the second situation is modeling water-oil, Buckley-Leverett (BL) displacement in waterflooding a fracture-dominated flow domain. The usefulness of matrix refinement will be illustrated using a three-node refinement of individual matrix blocks. Furthermore, this model was modified to account for matrix block size variability within each grid cell (in other words, statistical distribution of matrix size within each grid cell) using a discrete matrix-block-size distribution function. The paper will include two mathematical models, one unsteady-state pressure solution of the pressure diffusivity equation for use in rate transient analysis, and a second model, the Buckley-Leverett model to track saturation changes both in the reservoir fractures and within individual matrix blocks. To illustrate the effect of matrix heterogeneity on modeling results, we used three matrix bock sizes within each computation grid and one level of grid refinement for the individual matrix blocks. A critical issue in dual-porosity modeling is that much of the fluid interactions occur at the fracture-matrix interface. Therefore, refining the matrix block helps capture a more accurate transport of the fluid in-and-out of the matrix blocks. Our numerical results indicate that the none-refined matrix models provide only a poor approximation to saturation distribution within individual matrices. In other words, the saturation distribution is numerically dispersed; that is, no matrix refinement causes unwarranted large numerical dispersion in saturation distribution. Furthermore, matrix block size-distribution is more representative of fractured reservoirs.
{"title":"Matrix Refinement in Mass Transport Across Fracture-Matrix Interface: Application to Improved Oil Recovery in Fractured Reservoirs","authors":"Sarah Abdullatif Alruwayi, O. Uzun, H. Kazemi","doi":"10.2118/208038-ms","DOIUrl":"https://doi.org/10.2118/208038-ms","url":null,"abstract":"\u0000 In this paper, we will show that it is highly beneficial to model dual-porosity reservoirs using matrix refinement (similar to the multiple interacting continua, MINC, of Preuss, 1985) for water displacing oil. Two practical situations are considered. The first is the effect of matrix refinement on the unsteady-state pressure solution, and the second situation is modeling water-oil, Buckley-Leverett (BL) displacement in waterflooding a fracture-dominated flow domain.\u0000 The usefulness of matrix refinement will be illustrated using a three-node refinement of individual matrix blocks. Furthermore, this model was modified to account for matrix block size variability within each grid cell (in other words, statistical distribution of matrix size within each grid cell) using a discrete matrix-block-size distribution function. The paper will include two mathematical models, one unsteady-state pressure solution of the pressure diffusivity equation for use in rate transient analysis, and a second model, the Buckley-Leverett model to track saturation changes both in the reservoir fractures and within individual matrix blocks. To illustrate the effect of matrix heterogeneity on modeling results, we used three matrix bock sizes within each computation grid and one level of grid refinement for the individual matrix blocks.\u0000 A critical issue in dual-porosity modeling is that much of the fluid interactions occur at the fracture-matrix interface. Therefore, refining the matrix block helps capture a more accurate transport of the fluid in-and-out of the matrix blocks. Our numerical results indicate that the none-refined matrix models provide only a poor approximation to saturation distribution within individual matrices. In other words, the saturation distribution is numerically dispersed; that is, no matrix refinement causes unwarranted large numerical dispersion in saturation distribution. Furthermore, matrix block size-distribution is more representative of fractured reservoirs.","PeriodicalId":10967,"journal":{"name":"Day 1 Mon, November 15, 2021","volume":"112 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87805941","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}
Sheng Chen, Q. Zeng, Xiujiao Wang, Qing Yang, C. Dai, Wenke Li, R. Jiang
Practices of marine shale gas exploration and development in south China have proved that formation overpressure is the main controlling factor of shale gas enrichment and an indicator of good preservation condition. Accurate prediction of formation pressure before drilling is necessary for drilling safety and important for sweet spots predicting and horizontal wells deploying. However, the existing prediction methods of formation pore pressures all have defects, the prediction accuracy unsatisfactory for shale gas development. By means of rock mechanics analysis and related formulas, we derived a formula for calculating formation pore pressures. Through regional rock physical analysis, we determined and optimized the relevant parameters in the formula, and established a new formation pressure prediction model considering P-wave velocity, S-wave velocity and density. Based on regional exploration wells and 3D seismic data, we carried out pre-stack seismic inversion to obtain high-precision P-wave velocity, S-wave velocity and density data volumes. We utilized the new formation pressure prediction model to predict the pressure and the spatial distribution of overpressure sweet spots. Then, we applied the measured pressure data of three new wells to verify the predicted formation pressure by seismic data. The result shows that the new method has a higher accuracy. This method is qualified for safe drilling and prediction of overpressure sweet spots for shale gas development, so it is worthy of promotion.
{"title":"Using Seismic Data to Predict Shale Pore Pressure and Overpressure Sweet Spots: A Case Study from the Lower Silurian Longmaxi Formation in Sichuan Basin, China","authors":"Sheng Chen, Q. Zeng, Xiujiao Wang, Qing Yang, C. Dai, Wenke Li, R. Jiang","doi":"10.2118/208098-ms","DOIUrl":"https://doi.org/10.2118/208098-ms","url":null,"abstract":"\u0000 Practices of marine shale gas exploration and development in south China have proved that formation overpressure is the main controlling factor of shale gas enrichment and an indicator of good preservation condition. Accurate prediction of formation pressure before drilling is necessary for drilling safety and important for sweet spots predicting and horizontal wells deploying. However, the existing prediction methods of formation pore pressures all have defects, the prediction accuracy unsatisfactory for shale gas development. By means of rock mechanics analysis and related formulas, we derived a formula for calculating formation pore pressures. Through regional rock physical analysis, we determined and optimized the relevant parameters in the formula, and established a new formation pressure prediction model considering P-wave velocity, S-wave velocity and density. Based on regional exploration wells and 3D seismic data, we carried out pre-stack seismic inversion to obtain high-precision P-wave velocity, S-wave velocity and density data volumes. We utilized the new formation pressure prediction model to predict the pressure and the spatial distribution of overpressure sweet spots. Then, we applied the measured pressure data of three new wells to verify the predicted formation pressure by seismic data. The result shows that the new method has a higher accuracy. This method is qualified for safe drilling and prediction of overpressure sweet spots for shale gas development, so it is worthy of promotion.","PeriodicalId":10967,"journal":{"name":"Day 1 Mon, November 15, 2021","volume":"18 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89292428","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}
Kamaljit Singh, Raju Paul, F. Kamal, Ousama Takeiddine
Main Electrical Equipment Supplier (MEES) concept has been adopted on mega projects to facilitate overall electrical power system integration and standardization of items/systems within an EPC package as well as across multiple EPC packages and to avoid interface issues. A gap analysis has been performed in this paper based on experience on recent projects and recommendations are proposed as mitigation measures which will benefit Clients, Suppliers as well as FEED/EPC CONTRACTORs. Challenges faced during execution are categorized based on type as well as execution stages. Paper focusses on the major items to be defined as part of the frame agreement during MEES selection to minimize conflicts and issues later on. MEES package pricing (including material, non-material items and services) is done based on agreed Price Book. Gaps are identified as part of this paper including the methodology for change management. Scope limitation is also discussed in detail with clear objective of minimal risk to all stakeholders. Timing and responsibility of MEES selection, that is critical on mega projects with multiple packages, has also been analyzed. Concept of MEES is highly recommended on projects that have complex electrical power system, projects involving multiple EPC CONTRACTORs and also on the projects that are fast track in nature. It has been observed that most projects these days fall into one of these two categories which makes this paper even more relevant. However, execution of MEES package has vast scope for improvement. As part of MEES selection, the following shall be agreed and established, as a minimum: Selected make & model list of all the components along with the unit rates as part of MEES frame agreement that acceptable on project. Price book including all the main equipment envisaged on the project fully complying with project requirements. Comprehensive technical deviations acceptance list as part of MEES frame agreement. Technical compliance certificate indicating Vendor's compliance on the latest revision of Specifications/Data Sheets/ drawings associated with MEES Material Requisition (MR). Price Book should be sufficiently comprehensive without gaps to avoid variations after award. Most common gaps are identified and discussed in detail in the paper. It is highly recommended to select MEES during FEED stage (prior to bidding stage) in order to take advantages in terms of project schedule, change management and overall cost optimization. MEES concept, if managed with recommendations in this paper, can benefit all stakeholders.
{"title":"Gap Analysis on MEES Execution","authors":"Kamaljit Singh, Raju Paul, F. Kamal, Ousama Takeiddine","doi":"10.2118/207540-ms","DOIUrl":"https://doi.org/10.2118/207540-ms","url":null,"abstract":"\u0000 Main Electrical Equipment Supplier (MEES) concept has been adopted on mega projects to facilitate overall electrical power system integration and standardization of items/systems within an EPC package as well as across multiple EPC packages and to avoid interface issues. A gap analysis has been performed in this paper based on experience on recent projects and recommendations are proposed as mitigation measures which will benefit Clients, Suppliers as well as FEED/EPC CONTRACTORs.\u0000 Challenges faced during execution are categorized based on type as well as execution stages. Paper focusses on the major items to be defined as part of the frame agreement during MEES selection to minimize conflicts and issues later on. MEES package pricing (including material, non-material items and services) is done based on agreed Price Book. Gaps are identified as part of this paper including the methodology for change management. Scope limitation is also discussed in detail with clear objective of minimal risk to all stakeholders. Timing and responsibility of MEES selection, that is critical on mega projects with multiple packages, has also been analyzed.\u0000 Concept of MEES is highly recommended on projects that have complex electrical power system, projects involving multiple EPC CONTRACTORs and also on the projects that are fast track in nature. It has been observed that most projects these days fall into one of these two categories which makes this paper even more relevant. However, execution of MEES package has vast scope for improvement.\u0000 As part of MEES selection, the following shall be agreed and established, as a minimum:\u0000 Selected make & model list of all the components along with the unit rates as part of MEES frame agreement that acceptable on project. Price book including all the main equipment envisaged on the project fully complying with project requirements. Comprehensive technical deviations acceptance list as part of MEES frame agreement. Technical compliance certificate indicating Vendor's compliance on the latest revision of Specifications/Data Sheets/ drawings associated with MEES Material Requisition (MR).\u0000 Price Book should be sufficiently comprehensive without gaps to avoid variations after award. Most common gaps are identified and discussed in detail in the paper. It is highly recommended to select MEES during FEED stage (prior to bidding stage) in order to take advantages in terms of project schedule, change management and overall cost optimization.\u0000 MEES concept, if managed with recommendations in this paper, can benefit all stakeholders.","PeriodicalId":10967,"journal":{"name":"Day 1 Mon, November 15, 2021","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86000597","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. Ghedan, M. Surendra, Agustin Maqui, M. Elwan, Rami Kansao, Hesham Mousa, R. Jha, Mahmoud Korish, Feyi Olalotiti-Lawal, E. Shahin, Mohamed El Sayed, T. Eid, Lamia Rouis, Qingfeng Huang
Waterfloods are amongst the most widely implemented methods for oil field development. Despite their vast implementation, operational bottlenecks such as lack of surveillance and optimization tools to guide fast paced decisions render most of these sub-optimal. This paper presents a novel machine-learning, reduced-physics approach to optimize an exceptionally complex off-shore waterflood in the Gulf of Suez. Leveraging a hybrid data-driven and physics approach, the water flooding scheme in Nezzezat reservoir was optimized to improve reservoir voidage replacement, increase oil production, and reduce water production by identifying potential in wells. As a by-product of the study, a better understanding of the complex fault system was also achieved. Including the geological understanding and its uncertainty is one of the key elements that must be preserved. All geological attributes, along with production rates are used to solve for pressure and inter-well communication. This is later supplemented by machine-learning algorithm to solve for the fractional flow of inter-well connections. Combining the inter-well connectivity and fractional flow, an optimization was performed to reach the best possible conditions for oil gains and water-cut reduction. A global optimization is possible thanks to the low computational demand of this approach, as thousands to millions of realizations must be run to reach the best solution while satisfying all constraints. This is all done in a fraction of the time it takes to run a traditional reservoir simulation. For the present case, the paper will present the underlying physics and data-driven algorithms, along with the blind tests performed to validate the results. In addition to the method's inner workings, the paper will focus more on the results to guide operational decisions. This is inclusive of all the complex constraints of an offshore field, as well as the best reservoir management practices, when reaching optimal production and injection rates for each well. An increase in production was achieved with some reduction in water-cut, while honoring well and platform level limitations. While these represent the gains for a particular month, optimization scenarios can be run weekly or monthly to capture the dynamic nature of the problem and any operational limitations that might arise. The ability to update the models and run optimization scenarios effortlessly allows pro-active operational decisions to maximize the value of the asset. The approach followed in this paper solves for the critical physics of the problem and supplements the remaining with machine learning algorithms. This novel and extremely practical approach facilitate the decision making to operate the field optimally.
{"title":"Rapid and Efficient Waterflood Optimization Using Augmented AI Approach in a Complex Offshore Field","authors":"S. Ghedan, M. Surendra, Agustin Maqui, M. Elwan, Rami Kansao, Hesham Mousa, R. Jha, Mahmoud Korish, Feyi Olalotiti-Lawal, E. Shahin, Mohamed El Sayed, T. Eid, Lamia Rouis, Qingfeng Huang","doi":"10.2118/207458-ms","DOIUrl":"https://doi.org/10.2118/207458-ms","url":null,"abstract":"\u0000 Waterfloods are amongst the most widely implemented methods for oil field development. Despite their vast implementation, operational bottlenecks such as lack of surveillance and optimization tools to guide fast paced decisions render most of these sub-optimal. This paper presents a novel machine-learning, reduced-physics approach to optimize an exceptionally complex off-shore waterflood in the Gulf of Suez.\u0000 Leveraging a hybrid data-driven and physics approach, the water flooding scheme in Nezzezat reservoir was optimized to improve reservoir voidage replacement, increase oil production, and reduce water production by identifying potential in wells. As a by-product of the study, a better understanding of the complex fault system was also achieved. Including the geological understanding and its uncertainty is one of the key elements that must be preserved. All geological attributes, along with production rates are used to solve for pressure and inter-well communication. This is later supplemented by machine-learning algorithm to solve for the fractional flow of inter-well connections.\u0000 Combining the inter-well connectivity and fractional flow, an optimization was performed to reach the best possible conditions for oil gains and water-cut reduction. A global optimization is possible thanks to the low computational demand of this approach, as thousands to millions of realizations must be run to reach the best solution while satisfying all constraints. This is all done in a fraction of the time it takes to run a traditional reservoir simulation.\u0000 For the present case, the paper will present the underlying physics and data-driven algorithms, along with the blind tests performed to validate the results. In addition to the method's inner workings, the paper will focus more on the results to guide operational decisions. This is inclusive of all the complex constraints of an offshore field, as well as the best reservoir management practices, when reaching optimal production and injection rates for each well. An increase in production was achieved with some reduction in water-cut, while honoring well and platform level limitations. While these represent the gains for a particular month, optimization scenarios can be run weekly or monthly to capture the dynamic nature of the problem and any operational limitations that might arise. The ability to update the models and run optimization scenarios effortlessly allows pro-active operational decisions to maximize the value of the asset.\u0000 The approach followed in this paper solves for the critical physics of the problem and supplements the remaining with machine learning algorithms. This novel and extremely practical approach facilitate the decision making to operate the field optimally.","PeriodicalId":10967,"journal":{"name":"Day 1 Mon, November 15, 2021","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86668087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During the construction of pipelines for the transportation of oil and gas, the inspection of girth welds is a critical step to ensure the integrity and thereby the safety and durability of the pipeline. In this paper we present an advanced technology ‘IWEX’ for the non-destructive testing of welds based on 2D and 3D ultrasonic imaging. This technology allows for safe, fast, and accurate inspection whereby the results are presented comprehensively. This will be illustrated with results from a recent project. The IWEX technology is based on an ultrasonic inspection concept, whereby ‘fingerprints’ of ultrasonic signals are recorded, also referred to as ‘full matrix capture’ (FMC) data. Then, an image area is defined, consisting out of pixels over an area large enough to cover the inspection volume. With the FMC data, image amplitudes are calculated for each pixel so that the shape of geometry (back wall, front wall, cap, and root) and possible indications are revealed. As opposed to traditional ultrasonic testing strategies, the detection and sizing of indications is therefore less dependent on its orientation. The project concerned the inspection of J and V welds from a 5.56″ diameter carbon steel pipe with an 8.4mm wall thickness. The wall thickness is relatively thin compared to common inspection scopes. Therefore, the inspection set-up was adapted, and procedural changes were proposed. Consequently, additional validation efforts were required to demonstrate compliance with the required inspection standard; DNVGL-ST-F101: 2017. As part of this, welds were scanned with seeded indications and the reported locations were marked for macro slicing under witnessing of an independent representative from DNVGL. The resulting images from the indications in the welds showed great detail with respect to the position, orientation and height of the indications. A quantitative comparison with the results from the macro slices was performed, including a statistical analysis of the height sizing and depth positioning accuracies. From the analysis, it could be observed that the expected improvements with respect to the resolution and sizing accuracy were indeed achieved. Thereby, the procedure has proven to be adequate for the inspection of carbon steel girth welds within the thin wall thickness range (~6mm to ~15mm). The IWEX technology is a member of the upcoming inspection strategy based on imaging of ultrasonic FMC data. This strategy can be considered as the next step in the evolution of inspection strategies after phased array inspection. The IWEX technology has been witnessed and qualified by independent 3rd parties like DNVGL, this makes the IWEX technology unique in its kind and it opens opportunities for further acceptance in the industry and other inspection applications.
{"title":"Extending FMC Based Ultrasonic Imaging Practices to Smaller Wall Thickness","authors":"N. Pörtzgen, Olaf Solem","doi":"10.2118/207494-ms","DOIUrl":"https://doi.org/10.2118/207494-ms","url":null,"abstract":"\u0000 During the construction of pipelines for the transportation of oil and gas, the inspection of girth welds is a critical step to ensure the integrity and thereby the safety and durability of the pipeline. In this paper we present an advanced technology ‘IWEX’ for the non-destructive testing of welds based on 2D and 3D ultrasonic imaging. This technology allows for safe, fast, and accurate inspection whereby the results are presented comprehensively. This will be illustrated with results from a recent project.\u0000 The IWEX technology is based on an ultrasonic inspection concept, whereby ‘fingerprints’ of ultrasonic signals are recorded, also referred to as ‘full matrix capture’ (FMC) data. Then, an image area is defined, consisting out of pixels over an area large enough to cover the inspection volume. With the FMC data, image amplitudes are calculated for each pixel so that the shape of geometry (back wall, front wall, cap, and root) and possible indications are revealed. As opposed to traditional ultrasonic testing strategies, the detection and sizing of indications is therefore less dependent on its orientation.\u0000 The project concerned the inspection of J and V welds from a 5.56″ diameter carbon steel pipe with an 8.4mm wall thickness. The wall thickness is relatively thin compared to common inspection scopes. Therefore, the inspection set-up was adapted, and procedural changes were proposed. Consequently, additional validation efforts were required to demonstrate compliance with the required inspection standard; DNVGL-ST-F101: 2017. As part of this, welds were scanned with seeded indications and the reported locations were marked for macro slicing under witnessing of an independent representative from DNVGL.\u0000 The resulting images from the indications in the welds showed great detail with respect to the position, orientation and height of the indications. A quantitative comparison with the results from the macro slices was performed, including a statistical analysis of the height sizing and depth positioning accuracies. From the analysis, it could be observed that the expected improvements with respect to the resolution and sizing accuracy were indeed achieved. Thereby, the procedure has proven to be adequate for the inspection of carbon steel girth welds within the thin wall thickness range (~6mm to ~15mm).\u0000 The IWEX technology is a member of the upcoming inspection strategy based on imaging of ultrasonic FMC data. This strategy can be considered as the next step in the evolution of inspection strategies after phased array inspection. The IWEX technology has been witnessed and qualified by independent 3rd parties like DNVGL, this makes the IWEX technology unique in its kind and it opens opportunities for further acceptance in the industry and other inspection applications.","PeriodicalId":10967,"journal":{"name":"Day 1 Mon, November 15, 2021","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82546599","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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