Petrus In ‘T Panhuis, S. Mahajan, C. Prin, Ahmed Al Ajmi
Formation Integrity Tests (FIT) or Leak-Off Tests (LOT) are common techniques to reduce the uncertainty in Fracture Gradient (FG) prediction for well planning, but are usually performed at the casing shoe. This article will discuss the first examples of open-hole LOT and FIT in Petroleum Development Oman (PDO), targeting depleted formations in water injector or oil producer wells. The data was used to justify continued drilling of slim wells with two casing strings, where otherwise three casing strings would be required, provided dynamic wellbore strengthening is applied. In addition, the concept of static wellbore strengthening was also trialed for the first time in Oman, using the hesitation squeeze testing procedure, by which the effective leak-off pressure was incrementally increased to match the maximum ECD required for cementing.
{"title":"Impact of Static and Dynamic Wellbore Strengthening on Well Planning in Petroleum Development Oman","authors":"Petrus In ‘T Panhuis, S. Mahajan, C. Prin, Ahmed Al Ajmi","doi":"10.2118/207239-ms","DOIUrl":"https://doi.org/10.2118/207239-ms","url":null,"abstract":"\u0000 Formation Integrity Tests (FIT) or Leak-Off Tests (LOT) are common techniques to reduce the uncertainty in Fracture Gradient (FG) prediction for well planning, but are usually performed at the casing shoe. This article will discuss the first examples of open-hole LOT and FIT in Petroleum Development Oman (PDO), targeting depleted formations in water injector or oil producer wells. The data was used to justify continued drilling of slim wells with two casing strings, where otherwise three casing strings would be required, provided dynamic wellbore strengthening is applied.\u0000 In addition, the concept of static wellbore strengthening was also trialed for the first time in Oman, using the hesitation squeeze testing procedure, by which the effective leak-off pressure was incrementally increased to match the maximum ECD required for cementing.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86138604","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}
Shale ‘stability’ has been extensively studied the past few decades in an attempt to understand wellbore instability problems encountered while drilling. Drilling through shale is almost inevitable, it makes up 75 percent of sedimentary rocks. Shale tends to be characterized as having high in-situ stresses, fissile, laminated, with low permeability. However, not all shale are the same, and the problem herein lies where they are all treated as such, in which most cases, has shown to be ineffective. Ironically, shale is predominantly generalized as being "reactive/swelling". Even though this can be true, it is not always the case because not all shale is reactive! In reality, there are many different types of shale: ductile, brittle, carbonaceous, argillaceous, flysch, dispersive, kaolinitic, micro-fractured etc. This study aims to clear many misconceptions and define different types of shale (global case scenarios) and their failing mechanisms that lead to wellbore instability, formation damage and high drilling cost. Afterwards, solutions will be offered, from a filed operation perspective, which will provide guidelines for stabilizing various shale based on their failure mechanism. Furthermore, we will define the symptoms for shale instability and propose industry accepted remedies.
{"title":"What the Shale are We Talking About!","authors":"B. Hoxha, C. Rabe","doi":"10.2118/207412-ms","DOIUrl":"https://doi.org/10.2118/207412-ms","url":null,"abstract":"\u0000 Shale ‘stability’ has been extensively studied the past few decades in an attempt to understand wellbore instability problems encountered while drilling. Drilling through shale is almost inevitable, it makes up 75 percent of sedimentary rocks. Shale tends to be characterized as having high in-situ stresses, fissile, laminated, with low permeability. However, not all shale are the same, and the problem herein lies where they are all treated as such, in which most cases, has shown to be ineffective. Ironically, shale is predominantly generalized as being \"reactive/swelling\". Even though this can be true, it is not always the case because not all shale is reactive! In reality, there are many different types of shale: ductile, brittle, carbonaceous, argillaceous, flysch, dispersive, kaolinitic, micro-fractured etc. This study aims to clear many misconceptions and define different types of shale (global case scenarios) and their failing mechanisms that lead to wellbore instability, formation damage and high drilling cost. Afterwards, solutions will be offered, from a filed operation perspective, which will provide guidelines for stabilizing various shale based on their failure mechanism. Furthermore, we will define the symptoms for shale instability and propose industry accepted remedies.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86265190","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}
Stress-dependence of reservoir matrix and fractures can strongly affect the performance of multifractured horizontal wells (MFHWs) completed in unconventional hydrocarbon reservoirs. In order to model fluid flow in unconventional reservoirs exhibiting this stress-dependence, most traditional reservoir flow simulators, and many simulators described in published work, use conventional reservoir fluid flow model formulations. These formulations typically neglect the influence of the rate of change of volumetric strain of the reservoir matrix and fractures, even though reservoir stress and pressure change significantly during the course of production. As a result, the effect of matrix and fracture deformation on production is neglected, which can lead to errors in predicting production performance in most stress-sensitive reservoirs. To address this problem, some studies have proposed the use of porosity and transmissibility multipliers to model stress-sensitive reservoirs. However, in order to apply this approach, multipliers must be estimated from laboratory experiments, or used as a history-match parameter, possibly resulting in large errors in well performance predictions. Alternatively, fully-coupled, fully numerical geomechanical simulation can be performed, but these methods are computationally costly, and models are difficult to setup. This paper presents a new fully-coupled, two-way analytical modeling approach that can be used to simulate fluid flow in stress-sensitive unconventional reservoirs produced through MFHWs. The model couples poroelastic geomechanics theory with fluid flow formulations. The two-way coupled fluid flow-geomechanical analytical model is applied simultaneously to both the matrix and fracture regions. In the proposed algorithm, a porosity-compressibility coupling parameter for the two physical models is setup to update the stress- and pressure-dependent fracture/matrix properties iteratively, which are later used as input data for the fracture-matrix reservoir fluid flow model at each iteration step. The analytical approach developed for the fully-coupled, two-way analytical model, using the enhanced fracture region conceptual model, is validated by comparing the results with numerical simulation. Predictions using the fully-coupled enhanced fracture region model are then compared with the same enhanced fracture region model but with the conventional pressure-dependent modeling approach implemented. A sensitivity study performed by comparing the new fully-coupled model predictions with and without geomechanics effects accounted for reveals that, without geomechanics effects, production performance in stress-sensitive reservoirs might be overestimated. The study also demonstrates that use of the conventional stress-dependent modeling approach may cause production performance to be underestimated. Therefore, the proposed fully-coupled, two-way analytical model can be useful for practical engineering purposes.
{"title":"A Semi-Analytical Geomechanical Approach for Forecasting Production Performance in Multifractured Composite Systems","authors":"A. B. Lamidi, C. Clarkson","doi":"10.2118/208154-ms","DOIUrl":"https://doi.org/10.2118/208154-ms","url":null,"abstract":"\u0000 Stress-dependence of reservoir matrix and fractures can strongly affect the performance of multifractured horizontal wells (MFHWs) completed in unconventional hydrocarbon reservoirs. In order to model fluid flow in unconventional reservoirs exhibiting this stress-dependence, most traditional reservoir flow simulators, and many simulators described in published work, use conventional reservoir fluid flow model formulations. These formulations typically neglect the influence of the rate of change of volumetric strain of the reservoir matrix and fractures, even though reservoir stress and pressure change significantly during the course of production. As a result, the effect of matrix and fracture deformation on production is neglected, which can lead to errors in predicting production performance in most stress-sensitive reservoirs. To address this problem, some studies have proposed the use of porosity and transmissibility multipliers to model stress-sensitive reservoirs. However, in order to apply this approach, multipliers must be estimated from laboratory experiments, or used as a history-match parameter, possibly resulting in large errors in well performance predictions. Alternatively, fully-coupled, fully numerical geomechanical simulation can be performed, but these methods are computationally costly, and models are difficult to setup.\u0000 This paper presents a new fully-coupled, two-way analytical modeling approach that can be used to simulate fluid flow in stress-sensitive unconventional reservoirs produced through MFHWs. The model couples poroelastic geomechanics theory with fluid flow formulations. The two-way coupled fluid flow-geomechanical analytical model is applied simultaneously to both the matrix and fracture regions. In the proposed algorithm, a porosity-compressibility coupling parameter for the two physical models is setup to update the stress- and pressure-dependent fracture/matrix properties iteratively, which are later used as input data for the fracture-matrix reservoir fluid flow model at each iteration step.\u0000 The analytical approach developed for the fully-coupled, two-way analytical model, using the enhanced fracture region conceptual model, is validated by comparing the results with numerical simulation. Predictions using the fully-coupled enhanced fracture region model are then compared with the same enhanced fracture region model but with the conventional pressure-dependent modeling approach implemented. A sensitivity study performed by comparing the new fully-coupled model predictions with and without geomechanics effects accounted for reveals that, without geomechanics effects, production performance in stress-sensitive reservoirs might be overestimated. The study also demonstrates that use of the conventional stress-dependent modeling approach may cause production performance to be underestimated. Therefore, the proposed fully-coupled, two-way analytical model can be useful for practical engineering purposes.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88468508","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}
G. Wang, Dexiang Duan, Wanjun Li, Feng Qian, Zheng Qin, Zhao Zhong, Chuan Zhou, Baletabieke Bahedaer, Ning Jing, D. Ye, Qingyun Gao, Yue Xiao, Ganlu Li, Jitong Liu, Guobin Zhang, Shaohua Li
The overall liner cementing qualification rate is only 40% in Agadem block of Niger, The cement slurry system used in the field has a UCA transition time of 43min, and an expansion rate of -0.03% in 24h, which result in a poor anti-gas channeling performance. The expansive agent and the anti-gas channeling toughening agent of anti-channeling agent were optimized through experiment study. A novel micro-expansion anti-gas channel cement slurry system which is suitable for Agadem block was obtained through experiment optimization study: 100% G +2 ∼ 4% fluid loss agent +3 ∼ 4.5% anti-channeling agent +1 ∼ 2% expansion agent-100S +0.15 ∼ 0.4% retarder +0 ∼ 0.3% dispersant +0 ∼ 0.25% defoamer + water. This new cement system has a good anti-gas channeling performance, the cement strength is 24.5-35.0MPa after 24hrs, the UCA transition time is 16-18min, and the expansion rate is 1.5-1.7%. At the same time, a cementing prepad fluid suitable for the block and the micro-expansion cement slurry system is selected to ensure the performance of the cement slurry's anti-channeling performance. The field test results proofs the good performance of the new cement system. The cementing qualification rate of Koulele W-5 well is 96%, and the second interface cementation is Good. The cementing qualification rate of Trakes CN-1 well is 100% which second interface cementation is Excellent. This paper has positive guidance and reference for cementing in Agadem block.
{"title":"Research and Application of Micro-Expansion and Anti-Channeling Cement Slurry System in Agadem Oilfield","authors":"G. Wang, Dexiang Duan, Wanjun Li, Feng Qian, Zheng Qin, Zhao Zhong, Chuan Zhou, Baletabieke Bahedaer, Ning Jing, D. Ye, Qingyun Gao, Yue Xiao, Ganlu Li, Jitong Liu, Guobin Zhang, Shaohua Li","doi":"10.2118/207592-ms","DOIUrl":"https://doi.org/10.2118/207592-ms","url":null,"abstract":"\u0000 The overall liner cementing qualification rate is only 40% in Agadem block of Niger, The cement slurry system used in the field has a UCA transition time of 43min, and an expansion rate of -0.03% in 24h, which result in a poor anti-gas channeling performance. The expansive agent and the anti-gas channeling toughening agent of anti-channeling agent were optimized through experiment study. A novel micro-expansion anti-gas channel cement slurry system which is suitable for Agadem block was obtained through experiment optimization study: 100% G +2 ∼ 4% fluid loss agent +3 ∼ 4.5% anti-channeling agent +1 ∼ 2% expansion agent-100S +0.15 ∼ 0.4% retarder +0 ∼ 0.3% dispersant +0 ∼ 0.25% defoamer + water. This new cement system has a good anti-gas channeling performance, the cement strength is 24.5-35.0MPa after 24hrs, the UCA transition time is 16-18min, and the expansion rate is 1.5-1.7%. At the same time, a cementing prepad fluid suitable for the block and the micro-expansion cement slurry system is selected to ensure the performance of the cement slurry's anti-channeling performance. The field test results proofs the good performance of the new cement system. The cementing qualification rate of Koulele W-5 well is 96%, and the second interface cementation is Good. The cementing qualification rate of Trakes CN-1 well is 100% which second interface cementation is Excellent. This paper has positive guidance and reference for cementing in Agadem block.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80294770","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}
Oscar M. Molina, C. Mejia, M. Tyagi, F. Medellin, H. Elshahawi, Kumar Sujatha
The geothermal energy industry has never quite realized its true potential despite the seemingly magical promise of nonstop, 24/7 renewable energy sitting just below the surface of the Earth. In this paper, we discuss an integrated cloud-based workflow aimed at evaluating the cost-effectiveness of adopting geothermal production in low to medium enthalpy systems by either repurposing existing oil and gas wells or by co-producing thermal and fossil energy. The workflow introduces an automated and intrinsically secure decision-making process to convert mature oil and gas wells into geothermal wells, enabling both operational and financial assessment of the conversion process, whether partial or complete. The proposed workflow focuses on the reliability and transparency of fully automated technical processes for the geological, hydrodynamic, and mechanical configuration of the production system to ensure the financial success of the conversion project, in terms of heat production potential and cost of development. The decision-making portion of the workflow comprises the technical, social, environmental factors driving the return on investment for the total or partial conversion of wells to geothermal production. These components are evaluated using artificial intelligence (AI) algorithms that reduce bias in the decision-making process. The automated workflow involves assessment of the following: Heat Potential: A data-driven model to determine the geothermal heat potential using geological conditions from basin modeling and data from offset wells.Flow Modeling: An ultra-fast, physics-based modeling approach to determine pressure and temperature changes along wellbores to model fluid flow potential, thermal flux, and injection operations.Mechanical Integrity: Casing and completions integrity and configuration are embedded in the process for flow rates modeling.Environmental, Social, and Governance (ESG): A decision modeling framework is setup to ensure the transparent validation of the technical components and ESG factors, including potential for water pollution, carbon emissions, and social factors such as induced seismicity and ambient noise levels The assurance of key ESG metrics will ensure a viable and sustainable transition into a globally available low-carbon source of energy such as geothermal. Our novel cloud- based automated decision-making environment incorporates a blockchain framework to ensure transparency of technical-related processes and tasks, driving the financial success of the conversion project. Ultimately, our automated workflow is designed to encourage and support the widespread adoption of low-carbon energy in the oil and gas industry.
{"title":"Geothermal Production from Existing Oil and Gas Wells: A Sustainable Repurposing Model","authors":"Oscar M. Molina, C. Mejia, M. Tyagi, F. Medellin, H. Elshahawi, Kumar Sujatha","doi":"10.2118/207801-ms","DOIUrl":"https://doi.org/10.2118/207801-ms","url":null,"abstract":"\u0000 The geothermal energy industry has never quite realized its true potential despite the seemingly magical promise of nonstop, 24/7 renewable energy sitting just below the surface of the Earth. In this paper, we discuss an integrated cloud-based workflow aimed at evaluating the cost-effectiveness of adopting geothermal production in low to medium enthalpy systems by either repurposing existing oil and gas wells or by co-producing thermal and fossil energy. The workflow introduces an automated and intrinsically secure decision-making process to convert mature oil and gas wells into geothermal wells, enabling both operational and financial assessment of the conversion process, whether partial or complete.\u0000 The proposed workflow focuses on the reliability and transparency of fully automated technical processes for the geological, hydrodynamic, and mechanical configuration of the production system to ensure the financial success of the conversion project, in terms of heat production potential and cost of development. The decision-making portion of the workflow comprises the technical, social, environmental factors driving the return on investment for the total or partial conversion of wells to geothermal production. These components are evaluated using artificial intelligence (AI) algorithms that reduce bias in the decision-making process. The automated workflow involves assessment of the following: Heat Potential: A data-driven model to determine the geothermal heat potential using geological conditions from basin modeling and data from offset wells.Flow Modeling: An ultra-fast, physics-based modeling approach to determine pressure and temperature changes along wellbores to model fluid flow potential, thermal flux, and injection operations.Mechanical Integrity: Casing and completions integrity and configuration are embedded in the process for flow rates modeling.Environmental, Social, and Governance (ESG): A decision modeling framework is setup to ensure the transparent validation of the technical components and ESG factors, including potential for water pollution, carbon emissions, and social factors such as induced seismicity and ambient noise levels\u0000 The assurance of key ESG metrics will ensure a viable and sustainable transition into a globally available low-carbon source of energy such as geothermal. Our novel cloud- based automated decision-making environment incorporates a blockchain framework to ensure transparency of technical-related processes and tasks, driving the financial success of the conversion project. Ultimately, our automated workflow is designed to encourage and support the widespread adoption of low-carbon energy in the oil and gas industry.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82383939","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}
Salah Bahlany, Mohammed Maharbi, Saud Zakwani, F. Busaidi, Ferrante Benvenuti
Wellbore stability problems, such as stuck pipe and tight spots, are one of the most critical risks that impact drilling operations. Over several years, Oil and Gas Operator in Middle East has been facing problems associated with stuck pipe and tight spot events, which have a major impact on drilling efficiency, well cost, and the carbon footprint of drilling operations. On average, the operator loses 200 days a year (Non-Productive Time) on stuck pipe and associated fishing operations. Wellbore stability problems are hard to predict due to the varying conditions of drilling operations: different lithology, drilling parameters, pressures, equipment, shifting crews, and multiple well designs. All these factors make the occurrence of a stuck pipe quite hard to mitigate only through human intervention. For this reason, The operator decided to develop an artificial intelligence tool that leverages the whole breadth and depth of operator data (reports, sensor data, well engineering data, lithology data, etc.) in order to predict and prevent wellbore stability problems. The tool informs well engineers and rig crews about possible risks both during the well planning and well execution phase, suggesting possible mitigation actions to avoid getting stuck. Since the alarms are given ahead of the bit, several hours before the possible occurrence of the event, the well engineers and rig crews have ample time to react to the alarms and prevent its occurrence. So far, the tool has been deployed in a pilot phase on 38 wells giving 44 true alarms with a recall of 94%. Since mid-2021 operator has been rolling out the tool scaling to the whole drilling operations (over 40 rigs).
{"title":"STEP Change in Preventing Stuck Pipe and Tight Hole Events Using Machine Learning","authors":"Salah Bahlany, Mohammed Maharbi, Saud Zakwani, F. Busaidi, Ferrante Benvenuti","doi":"10.2118/207823-ms","DOIUrl":"https://doi.org/10.2118/207823-ms","url":null,"abstract":"\u0000 Wellbore stability problems, such as stuck pipe and tight spots, are one of the most critical risks that impact drilling operations. Over several years, Oil and Gas Operator in Middle East has been facing problems associated with stuck pipe and tight spot events, which have a major impact on drilling efficiency, well cost, and the carbon footprint of drilling operations. On average, the operator loses 200 days a year (Non-Productive Time) on stuck pipe and associated fishing operations.\u0000 Wellbore stability problems are hard to predict due to the varying conditions of drilling operations: different lithology, drilling parameters, pressures, equipment, shifting crews, and multiple well designs. All these factors make the occurrence of a stuck pipe quite hard to mitigate only through human intervention.\u0000 For this reason, The operator decided to develop an artificial intelligence tool that leverages the whole breadth and depth of operator data (reports, sensor data, well engineering data, lithology data, etc.) in order to predict and prevent wellbore stability problems.\u0000 The tool informs well engineers and rig crews about possible risks both during the well planning and well execution phase, suggesting possible mitigation actions to avoid getting stuck. Since the alarms are given ahead of the bit, several hours before the possible occurrence of the event, the well engineers and rig crews have ample time to react to the alarms and prevent its occurrence.\u0000 So far, the tool has been deployed in a pilot phase on 38 wells giving 44 true alarms with a recall of 94%. Since mid-2021 operator has been rolling out the tool scaling to the whole drilling operations (over 40 rigs).","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80196095","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}
Objectives/Scope: Oil and gas operators use a variety of reservoir engineering workflows in addition to the reservoir, production, and surface facility simulation tools to quantify reserves and complete field development planning activities. Reservoir fluid property data and models are fundamental input to all these workflows. Thus, it is important to understand the propagation of uncertainty in these various workflows arising from laboratory fluid property measured data and corresponding model uncertainty. The first step in understanding the impact of laboratory data uncertainty was to measure it, and as result, ADNOC Onshore undertook a detailed study to assess the performance of four selected reservoir fluid laboratories. The selected laboratories were evaluated using a blind round-robin study on stock tank liquid density and molar mass measurements, reservoir fluid flashed gas and flashed liquid C30+ reservoir composition gas chromatography measurements, and Constant Mass Expansion (CME) Pressure-Volume-Temperature (PVT) measurements using a variety of selected reservoir and pure components test fluids. Upon completion of the analytical study and establishing a range of measurement uncertainty, a sensitivity analysis study was completed using an equation of state (EoS) model to study the impact of reservoir fluid composition and molecular weight measurement uncertainty on EoS model predictions. Methods, Procedures, Process: A blind round test was designed and administered to assess the performance of the four laboratories. Strict confidentiality was maintained to conceal the identity of samples through blind test protocols. The round-robin tests were also witnessed by the researchers. The EoS sensitivity study was completed using the Peng Robinson EoS and a commercially available software package. Results, Observations, Conclusions: The results of the fully blind reservoir fluid laboratory tests along with the statistical analysis of uncertainties will be presented in this paper. One of the laboratories had a systemic deviation in the measured plus fraction composition on black oil reference standard samples. The plus fraction concentration is typically the largest weight percent component in black oil systems and, along with the plus fraction molar mass, plays a crucial role in establishing the mole percent overall reservoir fluid compositions. Another laboratory had systemic issues related to chromatogram component integration errors that resulted in inconsistent carbon number concentration trends for various components. All laboratories failed to produce consistent molecular weight measurements for the reference samples. Finally, one laboratory had a relative deviation for P-V measurements that were significantly outside the acceptable range. The EoS sensitivity study demonstrates that the fluid composition and stock tank oil molar mass measurements have a significant impact on EoS model predictions and hence the reservoir/production
{"title":"Accuracy and Precision of Reservoir Fluid Characterization Tests Through Blind Round-Robin Testing","authors":"A. Mawlod, Afzal Memon, J. Nighswander","doi":"10.2118/207749-ms","DOIUrl":"https://doi.org/10.2118/207749-ms","url":null,"abstract":"\u0000 Objectives/Scope: Oil and gas operators use a variety of reservoir engineering workflows in addition to the reservoir, production, and surface facility simulation tools to quantify reserves and complete field development planning activities. Reservoir fluid property data and models are fundamental input to all these workflows. Thus, it is important to understand the propagation of uncertainty in these various workflows arising from laboratory fluid property measured data and corresponding model uncertainty. The first step in understanding the impact of laboratory data uncertainty was to measure it, and as result, ADNOC Onshore undertook a detailed study to assess the performance of four selected reservoir fluid laboratories. The selected laboratories were evaluated using a blind round-robin study on stock tank liquid density and molar mass measurements, reservoir fluid flashed gas and flashed liquid C30+ reservoir composition gas chromatography measurements, and Constant Mass Expansion (CME) Pressure-Volume-Temperature (PVT) measurements using a variety of selected reservoir and pure components test fluids.\u0000 Upon completion of the analytical study and establishing a range of measurement uncertainty, a sensitivity analysis study was completed using an equation of state (EoS) model to study the impact of reservoir fluid composition and molecular weight measurement uncertainty on EoS model predictions.\u0000 Methods, Procedures, Process: A blind round test was designed and administered to assess the performance of the four laboratories. Strict confidentiality was maintained to conceal the identity of samples through blind test protocols. The round-robin tests were also witnessed by the researchers. The EoS sensitivity study was completed using the Peng Robinson EoS and a commercially available software package.\u0000 Results, Observations, Conclusions: The results of the fully blind reservoir fluid laboratory tests along with the statistical analysis of uncertainties will be presented in this paper. One of the laboratories had a systemic deviation in the measured plus fraction composition on black oil reference standard samples. The plus fraction concentration is typically the largest weight percent component in black oil systems and, along with the plus fraction molar mass, plays a crucial role in establishing the mole percent overall reservoir fluid compositions. Another laboratory had systemic issues related to chromatogram component integration errors that resulted in inconsistent carbon number concentration trends for various components. All laboratories failed to produce consistent molecular weight measurements for the reference samples. Finally, one laboratory had a relative deviation for P-V measurements that were significantly outside the acceptable range.\u0000 The EoS sensitivity study demonstrates that the fluid composition and stock tank oil molar mass measurements have a significant impact on EoS model predictions and hence the reservoir/production","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83909278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A single well from any mature field produces approximately 1.7 million Measurement While Drilling (MWD) data points. We either use cross-correlation and covariance measurement, or Long Short-Term Memory (LSTM) based Deep Learning algorithms to diagnose long sequences of extremely noisy data. LSTM's context size of 200 tokens barely accounts for the entire depth. Proposed work develops application of Transformer-based Deep Learning algorithm to diagnose and predict events in complex sequences of well-log data. Sequential models learn geological patterns and petrophysical trends to detect events across depths of well-log data. However, vanishing gradients, exploding gradients and the limits of convolutional filters, limit the diagnosis of ultra-deep wells in complex subsurface information. Vast number of operations required to detect events between two subsurface points at large separation limits them. Transformers-based Models (TbMs) rely on non-sequential modelling that uses self-attention to relate information from different positions in the sequence of well-log, allowing to create an end-to-end, non-sequential, parallel memory network. We use approximately 21 million data points from 21 wells of Volve for the experiment. LSTMs, in addition to auto-regression (AR), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) conventionally models the events in the time-series well-logs. However, complex global dependencies to detect events in heterogeneous subsurface are challenging for these sequence models. In the presented work we begin with one meter depth of data from Volve, an oil-field in the North Sea, and then proceed up to 1000 meters. Initially LSTMs and ARIMA models were acceptable, as depth increased beyond a few 100 meters their diagnosis started underperforming and a new methodology was required. TbMs have already outperformed several models in large sequences modelling for natural language processing tasks, thus they are very promising to model well-log data with very large depth separation. We scale features and labels according to the maximum and minimum value present in the training dataset and then use the sliding window to get training and evaluation data pairs from well-logs. Additional subsurface features were able to encode some information in the conventional sequential models, but the result did not compare significantly with the TbMs. TbMs achieved Root Mean Square Error of 0.27 on scale of (0-1) while diagnosing the depth up to 5000 meters. This is the first paper to show successful application of Transformer-based deep learning models for well-log diagnosis. Presented model uses a self-attention mechanism to learn complex dependencies and non-linear events from the well-log data. Moreover, the experimental setting discussed in the paper will act as a generalized framework for data from ultra-deep wells and their extremely heterogeneous subsurface environment.
{"title":"Transformer-Based Deep Learning Models for Well Log Processing and Quality Control by Modelling Global Dependence of the Complex Sequences","authors":"Ashutosh Kumar","doi":"10.2118/208109-ms","DOIUrl":"https://doi.org/10.2118/208109-ms","url":null,"abstract":"\u0000 A single well from any mature field produces approximately 1.7 million Measurement While Drilling (MWD) data points. We either use cross-correlation and covariance measurement, or Long Short-Term Memory (LSTM) based Deep Learning algorithms to diagnose long sequences of extremely noisy data. LSTM's context size of 200 tokens barely accounts for the entire depth. Proposed work develops application of Transformer-based Deep Learning algorithm to diagnose and predict events in complex sequences of well-log data.\u0000 Sequential models learn geological patterns and petrophysical trends to detect events across depths of well-log data. However, vanishing gradients, exploding gradients and the limits of convolutional filters, limit the diagnosis of ultra-deep wells in complex subsurface information. Vast number of operations required to detect events between two subsurface points at large separation limits them. Transformers-based Models (TbMs) rely on non-sequential modelling that uses self-attention to relate information from different positions in the sequence of well-log, allowing to create an end-to-end, non-sequential, parallel memory network. We use approximately 21 million data points from 21 wells of Volve for the experiment.\u0000 LSTMs, in addition to auto-regression (AR), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) conventionally models the events in the time-series well-logs. However, complex global dependencies to detect events in heterogeneous subsurface are challenging for these sequence models. In the presented work we begin with one meter depth of data from Volve, an oil-field in the North Sea, and then proceed up to 1000 meters. Initially LSTMs and ARIMA models were acceptable, as depth increased beyond a few 100 meters their diagnosis started underperforming and a new methodology was required. TbMs have already outperformed several models in large sequences modelling for natural language processing tasks, thus they are very promising to model well-log data with very large depth separation. We scale features and labels according to the maximum and minimum value present in the training dataset and then use the sliding window to get training and evaluation data pairs from well-logs. Additional subsurface features were able to encode some information in the conventional sequential models, but the result did not compare significantly with the TbMs. TbMs achieved Root Mean Square Error of 0.27 on scale of (0-1) while diagnosing the depth up to 5000 meters.\u0000 This is the first paper to show successful application of Transformer-based deep learning models for well-log diagnosis. Presented model uses a self-attention mechanism to learn complex dependencies and non-linear events from the well-log data. Moreover, the experimental setting discussed in the paper will act as a generalized framework for data from ultra-deep wells and their extremely heterogeneous subsurface environment.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82930398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Jacquemyn, G. Hampson, M. Jackson, D. Petrovskyy, S. Geiger, J. M. Machado Silva, S. Judice, F. Rahman, M. Sousa
Rapid Reservoir Modelling (RRM) is a software tool that combines geological operators and a flow diagnostics module with sketch-based interface and modelling technology. The geological operators account for all interactions of stratigraphic surfaces and ensure that the resulting 3D models are stratigraphically valid. The geological operators allow users to sketch in any order, from oldest to youngest, from large to small, or free of any prescribed order, depending on data-driven or concept-driven uncertainty in interpretation. Flow diagnostics assessment of the sketched models enforces the link between geological interpretation and flow behaviour without using time-consuming and computationally expensive workflows. Output of RRM models includes static measures of facies architecture, flow diagnostics and model elements that can be exported to industry-standard software. A deep-water case is presented to show how assessing the impact of different scenarios at a prototyping stage allows users to make informed decisions about subsequent modelling efforts and approaches. Furthermore, RRM provides a valuable method for training or to develop geological interpretation skills, in front of an outcrop or directly on subsurface data.
{"title":"Rapid Reservoir Modelling: Sketch-Based Geological Modelling with Fast Flow Diagnostics","authors":"C. Jacquemyn, G. Hampson, M. Jackson, D. Petrovskyy, S. Geiger, J. M. Machado Silva, S. Judice, F. Rahman, M. Sousa","doi":"10.2118/208041-ms","DOIUrl":"https://doi.org/10.2118/208041-ms","url":null,"abstract":"\u0000 Rapid Reservoir Modelling (RRM) is a software tool that combines geological operators and a flow diagnostics module with sketch-based interface and modelling technology. The geological operators account for all interactions of stratigraphic surfaces and ensure that the resulting 3D models are stratigraphically valid. The geological operators allow users to sketch in any order, from oldest to youngest, from large to small, or free of any prescribed order, depending on data-driven or concept-driven uncertainty in interpretation. Flow diagnostics assessment of the sketched models enforces the link between geological interpretation and flow behaviour without using time-consuming and computationally expensive workflows. Output of RRM models includes static measures of facies architecture, flow diagnostics and model elements that can be exported to industry-standard software. A deep-water case is presented to show how assessing the impact of different scenarios at a prototyping stage allows users to make informed decisions about subsequent modelling efforts and approaches. Furthermore, RRM provides a valuable method for training or to develop geological interpretation skills, in front of an outcrop or directly on subsurface data.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80945454","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}
The success of any matrix treatment depends upon the complete coverage of all zones. Consequently, the selection of the diversion technology is critical for treatment success. While various types of diverting agents are commercially available, the proper selection of optimal diverter depends on many factors, including well completion and history, compatibility with reservoir and treatment fluids, treatment objectives, operational constraints, and safety and environment considerations. The study will cover five major types of non-mechanical diversion technologies considered as potential solutions for offshore deepwater oil reservoirs: dynamic diversion, relative permeability modifiers (RPM), viscoelastic surfactants (VES), particulate diversion, and perforation diversion. All of them, but a dynamic diversion, are based on different chemicals or products to be added to the injected treatment fluid, and occasionally some can be complementary to each other. Given the offshore and deepwater settings, mechanical diversion techniques were not covered in the study, aiming to find a solution that would achieve acceptable diversion while minimizing operational effort, which would enable riser-less intervention and the use of light intervention techniques. This study was driven by the need to effectively stimulate a 500ft of a cased and perforated interval with a permeability of 500 md, and injection rate limited to 16 bpm due to completion limitations. The sandstone formation, with static in situ temperature of 270F, was far beyond the applicability of dynamic diversion and, to achieve the desired full coverage for the planned scale inhibition treatment required and combination with another diverter system was needed. The process applied included compatibility tests, regained permeability tests, and test well trials. Depending on the specific diversion product analyzed the testing procedures were adapted to obtain the information to properly guide to the optimal solution.
{"title":"Several Decades of Fluid Diversion Evolution, Is There a Good Solution?","authors":"A. Casero, A. Gomaa","doi":"10.2118/207953-ms","DOIUrl":"https://doi.org/10.2118/207953-ms","url":null,"abstract":"\u0000 The success of any matrix treatment depends upon the complete coverage of all zones. Consequently, the selection of the diversion technology is critical for treatment success. While various types of diverting agents are commercially available, the proper selection of optimal diverter depends on many factors, including well completion and history, compatibility with reservoir and treatment fluids, treatment objectives, operational constraints, and safety and environment considerations.\u0000 The study will cover five major types of non-mechanical diversion technologies considered as potential solutions for offshore deepwater oil reservoirs: dynamic diversion, relative permeability modifiers (RPM), viscoelastic surfactants (VES), particulate diversion, and perforation diversion. All of them, but a dynamic diversion, are based on different chemicals or products to be added to the injected treatment fluid, and occasionally some can be complementary to each other.\u0000 Given the offshore and deepwater settings, mechanical diversion techniques were not covered in the study, aiming to find a solution that would achieve acceptable diversion while minimizing operational effort, which would enable riser-less intervention and the use of light intervention techniques.\u0000 This study was driven by the need to effectively stimulate a 500ft of a cased and perforated interval with a permeability of 500 md, and injection rate limited to 16 bpm due to completion limitations. The sandstone formation, with static in situ temperature of 270F, was far beyond the applicability of dynamic diversion and, to achieve the desired full coverage for the planned scale inhibition treatment required and combination with another diverter system was needed.\u0000 The process applied included compatibility tests, regained permeability tests, and test well trials. Depending on the specific diversion product analyzed the testing procedures were adapted to obtain the information to properly guide to the optimal solution.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89480304","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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