Reservoir uncertainties, high water cut, completion integrity along with declining production are the major challenges of a mature field. These integrated with dying facilities and poor field production are key issues that each oil and gas company is facing these days. Arresting production decline is an inevitable objective, but with the existing techniques/steps involved, it becomes a cumbersome and exorbitant affair for the operators to meet their requirements. In addition, incompetent and flawed well data makes it more challenging to analyze mature fields. Although flow rate data is the most easily accessible data for mature fields, the absence of pressure data (flowing bottom-hole or wellhead pressure) remains a big obstacle for the application of conventional production enhancement and well screening strategies for most of the mature fields. A real-time optimization tool is thus constructed by developing a hybrid modelling technique that encapsulates Kriging and Fuzzy Logic to account for the imprecisions and uncertainties involved while identification of subsurface locations for production optimization of a mature field using only production data. The data from the existing wells in the field is used to generate a membership function based on its historical performance and productivity, thereby generating a spatial map of prospective areas, where secondary development operations can be taken up for production optimization.
{"title":"Unlocking Field Potential of Mature Fields Using Hybrid Fuzzy Modelling and Kriging Method","authors":"Saransh Surana","doi":"10.2118/208631-stu","DOIUrl":"https://doi.org/10.2118/208631-stu","url":null,"abstract":"\u0000 Reservoir uncertainties, high water cut, completion integrity along with declining production are the major challenges of a mature field. These integrated with dying facilities and poor field production are key issues that each oil and gas company is facing these days. Arresting production decline is an inevitable objective, but with the existing techniques/steps involved, it becomes a cumbersome and exorbitant affair for the operators to meet their requirements. In addition, incompetent and flawed well data makes it more challenging to analyze mature fields. Although flow rate data is the most easily accessible data for mature fields, the absence of pressure data (flowing bottom-hole or wellhead pressure) remains a big obstacle for the application of conventional production enhancement and well screening strategies for most of the mature fields.\u0000 A real-time optimization tool is thus constructed by developing a hybrid modelling technique that encapsulates Kriging and Fuzzy Logic to account for the imprecisions and uncertainties involved while identification of subsurface locations for production optimization of a mature field using only production data. The data from the existing wells in the field is used to generate a membership function based on its historical performance and productivity, thereby generating a spatial map of prospective areas, where secondary development operations can be taken up for production optimization.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76421908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes the transformational application of Artificial Intelligence (AI) in Equinor's annual well planning and maturation process. Well planning is a complex decision-making process, like many other processes in the industry. There are thousands of choices, conflicting business drivers, lots of uncertainty, and hidden bias. These complexities all add up, which makes good decision making very hard. In this application, AI has been used for automated and unbiased evaluation of the full solution space, with the objective to optimize the selection of drilling campaigns while taking into account complex issues such as anti-collision with existing wells, drilling hazards and trade-offs between cost, value and risk. Designing drillable well trajectories involves a sequence of decisions, which makes the process very suitable for AI algorithms. Different solver architectures, or algorithms, can be used to play this game. This is similar to how companies such as Google-owned DeepMind develop customized solvers for games such as Go and StarCraft. The chosen method is a Tree Search algorithm with an evolutionary layer on top, providing a good balance in terms of performance (i.e., speed) vs. exploration capability (i.e., it looks "wide" in the option space). The algorithm has been deployed in a full stack web-based application that allows users to follow an end-2-end workflow: from defining well trajectory design rules and constraints to running the AI engine and evaluating results to the optimization of multi-well drilling campaigns based on risk, value and cost objectives. The full-size paper describes different Norwegian Continental Shelf (NCS) use cases of this AI assisted well trajectory planning. Results to-date indicate significant CAPEX savings potential and step-change improvements in decision speed (months to days) compared to routine manual workflows. There are very limited real transformative examples of Artificial Intelligence in multi- disciplinary workflows. This paper therefore gives a unique insight how a combination of data science, domain expertise and end user feedback can lead to powerful and transformative AI solutions – implemented at scale within an existing organization.
{"title":"Novel Application of Artificial Intelligence with Potential to Transform Well Planning Workflows on the Norwegian Continental Shelf","authors":"J. G. Vabø, E. Delaney, T. Savel, N. Dolle","doi":"10.2118/206339-ms","DOIUrl":"https://doi.org/10.2118/206339-ms","url":null,"abstract":"\u0000 This paper describes the transformational application of Artificial Intelligence (AI) in Equinor's annual well planning and maturation process.\u0000 Well planning is a complex decision-making process, like many other processes in the industry. There are thousands of choices, conflicting business drivers, lots of uncertainty, and hidden bias. These complexities all add up, which makes good decision making very hard.\u0000 In this application, AI has been used for automated and unbiased evaluation of the full solution space, with the objective to optimize the selection of drilling campaigns while taking into account complex issues such as anti-collision with existing wells, drilling hazards and trade-offs between cost, value and risk.\u0000 Designing drillable well trajectories involves a sequence of decisions, which makes the process very suitable for AI algorithms. Different solver architectures, or algorithms, can be used to play this game. This is similar to how companies such as Google-owned DeepMind develop customized solvers for games such as Go and StarCraft.\u0000 The chosen method is a Tree Search algorithm with an evolutionary layer on top, providing a good balance in terms of performance (i.e., speed) vs. exploration capability (i.e., it looks \"wide\" in the option space).\u0000 The algorithm has been deployed in a full stack web-based application that allows users to follow an end-2-end workflow: from defining well trajectory design rules and constraints to running the AI engine and evaluating results to the optimization of multi-well drilling campaigns based on risk, value and cost objectives.\u0000 The full-size paper describes different Norwegian Continental Shelf (NCS) use cases of this AI assisted well trajectory planning.\u0000 Results to-date indicate significant CAPEX savings potential and step-change improvements in decision speed (months to days) compared to routine manual workflows.\u0000 There are very limited real transformative examples of Artificial Intelligence in multi- disciplinary workflows. This paper therefore gives a unique insight how a combination of data science, domain expertise and end user feedback can lead to powerful and transformative AI solutions – implemented at scale within an existing organization.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76435652","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}
Unconventional resources, which are typically characterized by poor porosity and permeability are being economically developed only after the introduction of hydraulic fracturing (HF) technology, which is required to stimulate the hydrocarbon flow from these impermeable/tight reservoir rocks. Since 1960, HF has been extensively used in the industry. HF is the process of (1) injecting viscous gel fluids through the wellbore into the subterranean hydrocarbon formation, at high pressures sufficient enough to exceed tensile strength of the rock and hydraulically induce cracks/fractures (2) followed by injecting proppant-laden fluid into the open fractures and packing up the fracture with proppant pack, after the injected fluid leaks off into formation. The resultant proppant pack keeps the induced fracture propped open and thus creates a highly conductive flow path for the hydrocarbon to flow from the far-field subterranean formation into the wellbore. Most the modern wells in unconventional reservoirs are horizontal/near-horizontal wells that are completed with large multiple HF treatments across the entire length of the horizontal wellbore (lateral), to increase the reservoir contact per well. Productivity of these wells is dictated by the stimulated reservoir volume (SRV), which is dependent on the number of fractures and conductive hydraulic fracture surface area of each fracture that is propped open. Therefore, estimation of the hydraulic fracture geometry (HFG) dimensions has become very critical for any unconventional field development. Key dimensions are hydraulic fracture length, height, and orientation, which are required to assess the optimum configuration of fracturing, well completion, and reservoir management strategy to achieve maximum production. Designs can be assessed based on HFG observations, and infill well trajectories, spacing, etc. can be planned for further field development. This workflow proposes a method to estimate and model all or at least two parameters of HFG in predominantly horizontal or nearly horizontal wells by use of interwell electromagnetic recordings. The foundation of this workflow is the difference in salinity, or more precisely resistivity, of the fracturing fluid and the resident fluid (hydrocarbon or formation water). The fracturing fluid is usually significantly less resistive than the hydrocarbon that is the dominant resident fluid where fracturing is usually conducted, or less resistive than the formation water in case the HF occurs in high water saturation regions. Therefore, the resistivity contrast between the two fluids will demarcate the boundary of hydraulic fractures and thus help in precisely modeling some or all parameters of HFG. The interwell recordings can be interpreted along a 2D plane between the two wells, one of them bearing the transmitter and the other with the receiver. The interpretations along a 2D plane can be used to calibrate a 3D unstructured HF model, thereby introducing
{"title":"High Accuracy Estimation of Hydraulic Fracture Geometry Using Crosswell Electromagnetics","authors":"Shubham Mishra, V. Reddy","doi":"10.2118/206266-ms","DOIUrl":"https://doi.org/10.2118/206266-ms","url":null,"abstract":"\u0000 Unconventional resources, which are typically characterized by poor porosity and permeability are being economically developed only after the introduction of hydraulic fracturing (HF) technology, which is required to stimulate the hydrocarbon flow from these impermeable/tight reservoir rocks. Since 1960, HF has been extensively used in the industry. HF is the process of (1) injecting viscous gel fluids through the wellbore into the subterranean hydrocarbon formation, at high pressures sufficient enough to exceed tensile strength of the rock and hydraulically induce cracks/fractures (2) followed by injecting proppant-laden fluid into the open fractures and packing up the fracture with proppant pack, after the injected fluid leaks off into formation. The resultant proppant pack keeps the induced fracture propped open and thus creates a highly conductive flow path for the hydrocarbon to flow from the far-field subterranean formation into the wellbore.\u0000 Most the modern wells in unconventional reservoirs are horizontal/near-horizontal wells that are completed with large multiple HF treatments across the entire length of the horizontal wellbore (lateral), to increase the reservoir contact per well. Productivity of these wells is dictated by the stimulated reservoir volume (SRV), which is dependent on the number of fractures and conductive hydraulic fracture surface area of each fracture that is propped open. Therefore, estimation of the hydraulic fracture geometry (HFG) dimensions has become very critical for any unconventional field development. Key dimensions are hydraulic fracture length, height, and orientation, which are required to assess the optimum configuration of fracturing, well completion, and reservoir management strategy to achieve maximum production. Designs can be assessed based on HFG observations, and infill well trajectories, spacing, etc. can be planned for further field development.\u0000 This workflow proposes a method to estimate and model all or at least two parameters of HFG in predominantly horizontal or nearly horizontal wells by use of interwell electromagnetic recordings. The foundation of this workflow is the difference in salinity, or more precisely resistivity, of the fracturing fluid and the resident fluid (hydrocarbon or formation water). The fracturing fluid is usually significantly less resistive than the hydrocarbon that is the dominant resident fluid where fracturing is usually conducted, or less resistive than the formation water in case the HF occurs in high water saturation regions. Therefore, the resistivity contrast between the two fluids will demarcate the boundary of hydraulic fractures and thus help in precisely modeling some or all parameters of HFG. The interwell recordings can be interpreted along a 2D plane between the two wells, one of them bearing the transmitter and the other with the receiver. The interpretations along a 2D plane can be used to calibrate a 3D unstructured HF model, thereby introducing ","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78738737","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}
Removing stagnant liquid in a loaded horizontal gas well remains an unsolved challenge. Current practices for horizontal well deliquification are limited in terms of reliability and continuity, resulting on increased OPEX and CAPEX, behind down time and additional equipment installation. Experimental evaluation of a proposed artificial lift method for horizontal well deliquification, showed average removal efficiencies of 75% of the stagnant liquid volume. The experimental facility consisted of an experimental flow loop, that replicates conditions of liquid-loaded horizontal wells, with a horizontal section of 40 feet and a vertical section of 40 feet. The method is based on the chamber lift principles, using intermittent injection of gas at high pressure and low volumetric flow rates to the horizontal section of the well. Removal efficiency increased by 12% by using saccharidic additives and sodium chloride, to increase the surface tension between the injected gas (compressed air) and the liquid (water). This work presents a mechanistic model of the proposed artificial lift method, based on the momentum balance of the gas and the liquid slug flowing along the horizontal and vertical sections of the system, including numerical regressions for the prediction of the surface tension and viscosity of the liquid mixture as a function of temperature and the concentration of the tested additives. The model is used to determine the required available injection pressure at surface, and the location of the valve mandrel, as same as to estimate the removed liquid volume, discharge volumetric rate, and discharge pressure of the liquid slug at the surface facilities. The model is validated against experimental data obtained from the experimental flow loop.
{"title":"Mechanistic Model for the Design and Operation of an Intermittent Gas Lift System for Liquid Loaded Horizontal Gas Wells","authors":"Daniel Croce, L. Zerpa","doi":"10.2118/205962-ms","DOIUrl":"https://doi.org/10.2118/205962-ms","url":null,"abstract":"\u0000 Removing stagnant liquid in a loaded horizontal gas well remains an unsolved challenge. Current practices for horizontal well deliquification are limited in terms of reliability and continuity, resulting on increased OPEX and CAPEX, behind down time and additional equipment installation. Experimental evaluation of a proposed artificial lift method for horizontal well deliquification, showed average removal efficiencies of 75% of the stagnant liquid volume. The experimental facility consisted of an experimental flow loop, that replicates conditions of liquid-loaded horizontal wells, with a horizontal section of 40 feet and a vertical section of 40 feet. The method is based on the chamber lift principles, using intermittent injection of gas at high pressure and low volumetric flow rates to the horizontal section of the well. Removal efficiency increased by 12% by using saccharidic additives and sodium chloride, to increase the surface tension between the injected gas (compressed air) and the liquid (water). This work presents a mechanistic model of the proposed artificial lift method, based on the momentum balance of the gas and the liquid slug flowing along the horizontal and vertical sections of the system, including numerical regressions for the prediction of the surface tension and viscosity of the liquid mixture as a function of temperature and the concentration of the tested additives. The model is used to determine the required available injection pressure at surface, and the location of the valve mandrel, as same as to estimate the removed liquid volume, discharge volumetric rate, and discharge pressure of the liquid slug at the surface facilities. The model is validated against experimental data obtained from the experimental flow loop.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76750848","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 production and transportation of high viscosity liquid/gas two-phase along petroleum production system is a challenging operation due to the lack of understanding the flow behavior and characteristics. In particular, accurate prediction of two-phase slug length in pipes is crucial to efficiently operate and safely design oil well and separation facilities. The objective of this study is to develop a mechanistic model to predict high viscosity liquid slug length in pipelines and to optimize the proper set of closure relationships required to ensure high accuracy prediction. A large high viscosity liquid slug length database is collected and presented in this study, against which the proposed model is validated and compared with other models. A mechanistic slug length model is derived based on the first principles of mass and momentum balances over a two-phase slug unit, which requires a set of closure relationships of other slug characteristics. To select the proper set of closure relationships, a numerical optimization is carried out using a large slug length dataset to minimize the prediction error. Thousands of combinations of various slug flow closure relationships were evaluated to identify the most appropriate relationships for the proposed slug length model under high viscosity slug length condition. Results show that the proposed slug length mechanistic model is applicable for a wide range of liquid viscosities and is sensitive to the selected closure relationships. Results revealed that the optimum closure relationships combination is Archibong-Eso et al. (2018) for slug frequency, Malnes (1983) for slug liquid holdup, Jeyachandra et al. (2012) for drift velocity, and Nicklin et al. (1962) for the distribution coefficient. Using the above set of closure relationships, model validation yields 37.8% absolute average percent error, outperforming all existing slug length models.
{"title":"Prediction of High Viscosity Liquid/Gas Two-Phase Slug Length in Horizontal and Slightly Inclined Pipelines","authors":"O. Shaaban, E. Al-Safran","doi":"10.2118/206280-ms","DOIUrl":"https://doi.org/10.2118/206280-ms","url":null,"abstract":"\u0000 The production and transportation of high viscosity liquid/gas two-phase along petroleum production system is a challenging operation due to the lack of understanding the flow behavior and characteristics. In particular, accurate prediction of two-phase slug length in pipes is crucial to efficiently operate and safely design oil well and separation facilities. The objective of this study is to develop a mechanistic model to predict high viscosity liquid slug length in pipelines and to optimize the proper set of closure relationships required to ensure high accuracy prediction. A large high viscosity liquid slug length database is collected and presented in this study, against which the proposed model is validated and compared with other models. A mechanistic slug length model is derived based on the first principles of mass and momentum balances over a two-phase slug unit, which requires a set of closure relationships of other slug characteristics. To select the proper set of closure relationships, a numerical optimization is carried out using a large slug length dataset to minimize the prediction error. Thousands of combinations of various slug flow closure relationships were evaluated to identify the most appropriate relationships for the proposed slug length model under high viscosity slug length condition. Results show that the proposed slug length mechanistic model is applicable for a wide range of liquid viscosities and is sensitive to the selected closure relationships. Results revealed that the optimum closure relationships combination is Archibong-Eso et al. (2018) for slug frequency, Malnes (1983) for slug liquid holdup, Jeyachandra et al. (2012) for drift velocity, and Nicklin et al. (1962) for the distribution coefficient. Using the above set of closure relationships, model validation yields 37.8% absolute average percent error, outperforming all existing slug length models.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"168 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76641777","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}
Water-alternating-gas (WAG) injection, both miscible and immiscible, is a widely used enhanced oil recovery method with over 80 field cases. Despite its prevalence, the numerical modeling of the physical processes involved remains poorly understood, and existing models often lack predictability. Part of the complexity stems from the component exchange between gas and oil and the hysteretic relative permeability effects. Thus, improving the reliability of numerical models requires the calibration of the equation of state (EOS) against phase behavior data from swelling/extraction and slim-tube tests, and the calibration of the three-phase relative permeability model against WAG coreflood experiments. This paper presents the results and interpretation of a complete set of two-phase and thee-phase displacement experiments on mixed-wet carbonate rocks. The three-phase WAG experiments were conducted on the same composite core at near-miscible reservoir condition; experiments differ in the injection order and length of their injection cycles. First, the two-phase water/oil and gas/oil displacement experiments and first cycles of WAG were used to estimate the two-phase relative permeabilities. Then, a history matching procedure over the full set of WAG cycles was carried out to tune the Larsen and Skauge WAG hysteresis model—namely the Land gas trapping parameter, the gas reduction exponent, the residual oil reduction factor and three-phase water relative permeability. The second part of this paper is dedicated to the value of information (VOI) analysis of the coreflood work program to assist the decision to proceed with a capital intensive WAG pilot at an offshore oilfield. Stochastic simulation of WAG injection using a fine scale sector model allowed to quantify the reduction in the range of uncertainty of key metrics—such as oil recovery, peak gas production and injectivity—linked with the additional SCAL information. The current study highlights the impact of the WAG injection sequence on the oil recovery and trapping mechanism. In addition, it is shown that the relative permeabilities and hysteresis model calibrated on one particular set of injection cycles fail to capture the WAG performance when the injection cycles are altered. Finally, the VOI methodology demonstrated the value enhancement from the coreflood work program.
{"title":"Experimental Study and History Match of Near-Miscible WAG Coreflood Experiments on Mixed-Wet Carbonate Rocks","authors":"M. E. El Faidouzi","doi":"10.2118/206307-ms","DOIUrl":"https://doi.org/10.2118/206307-ms","url":null,"abstract":"\u0000 Water-alternating-gas (WAG) injection, both miscible and immiscible, is a widely used enhanced oil recovery method with over 80 field cases. Despite its prevalence, the numerical modeling of the physical processes involved remains poorly understood, and existing models often lack predictability. Part of the complexity stems from the component exchange between gas and oil and the hysteretic relative permeability effects. Thus, improving the reliability of numerical models requires the calibration of the equation of state (EOS) against phase behavior data from swelling/extraction and slim-tube tests, and the calibration of the three-phase relative permeability model against WAG coreflood experiments.\u0000 This paper presents the results and interpretation of a complete set of two-phase and thee-phase displacement experiments on mixed-wet carbonate rocks. The three-phase WAG experiments were conducted on the same composite core at near-miscible reservoir condition; experiments differ in the injection order and length of their injection cycles.\u0000 First, the two-phase water/oil and gas/oil displacement experiments and first cycles of WAG were used to estimate the two-phase relative permeabilities. Then, a history matching procedure over the full set of WAG cycles was carried out to tune the Larsen and Skauge WAG hysteresis model—namely the Land gas trapping parameter, the gas reduction exponent, the residual oil reduction factor and three-phase water relative permeability.\u0000 The second part of this paper is dedicated to the value of information (VOI) analysis of the coreflood work program to assist the decision to proceed with a capital intensive WAG pilot at an offshore oilfield. Stochastic simulation of WAG injection using a fine scale sector model allowed to quantify the reduction in the range of uncertainty of key metrics—such as oil recovery, peak gas production and injectivity—linked with the additional SCAL information.\u0000 The current study highlights the impact of the WAG injection sequence on the oil recovery and trapping mechanism. In addition, it is shown that the relative permeabilities and hysteresis model calibrated on one particular set of injection cycles fail to capture the WAG performance when the injection cycles are altered. Finally, the VOI methodology demonstrated the value enhancement from the coreflood work program.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77046655","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}
Due to challenging market conditions, the drilling and completion industry has needed to put forth innovative deployment strategies in horizontal multi-stage completions. In difficult wellbores, the traditional method for deploying liners was to run drill pipe. The case studies discussed in this paper detail an alternative method to deploy liners in a single trip on the tieback string so the operator can reduce the overall costs of deployment. Previously, this was not practical because the tieback string weight could not overcome the wellbore friction in horizontal applications. In each case, a flotation collar is required to ensure there is enough hook load for deployment of the liner system. The flotation collars used are an interventionless design, utilizing a tempered glass barrier that shatters at a pre-determined applied pressure. The glass debris can be easily circulated through the well without damaging downhole components. This is done commonly on cemented liner and cemented monobore installations, but more rarely with open hole multi-stage completions. For open hole multi-stage completions, the initial installation typically requires an activation tool at the bottom of the well to set the hydraulically activated equipment above. Multiple validation tests were completed prior to installation by using an activation tool and flotation collar to ensure the debris could be safely circulated through the internals without closing the activation tool. These activation tools have relatively limited flow area and could cause an issue if the glass debris were to accumulate and shift it closed prematurely. Premature closing of the tool would leave expensive drilling fluids in contact with the reservoir, potentially harming production. For the test, the flotation collar was placed only two pup joints away from the activation tool, resulting in a worst-case scenario where a large amount of debris could potentially encounter the internals of the activation tool at one time. In a downhole environment the flotation collar is typically installed near the build or heel of the well, depending on wellbore geometry. The testing was successfully completed, and the activation tool showed no signs of loading. This resulted in a full-scale trial in the field where a 52 stage, open hole (OH) multi-stage fracturing (MSF) liner was deployed using this technology. Through close collaboration with the operator, an acceptable procedure was established to safely circulate the glass debris and further limit the risk of prematurely closing the activation tool. This paper discusses the OH and cemented MSF deployment challenges, detailed lab testing, and field qualification trials for the single trip deployed system. It also highlights operational procedures and best practices when deploying the system in this fashion. A method to calibrate a torque and drag model will also be explored as part of this discussion.
{"title":"Single Trip Deployment of Multi-Stage Completion Liners Through the Used of Interventionless Flotation Collars","authors":"W. Tait, M. Munawar","doi":"10.2118/205957-ms","DOIUrl":"https://doi.org/10.2118/205957-ms","url":null,"abstract":"\u0000 Due to challenging market conditions, the drilling and completion industry has needed to put forth innovative deployment strategies in horizontal multi-stage completions. In difficult wellbores, the traditional method for deploying liners was to run drill pipe. The case studies discussed in this paper detail an alternative method to deploy liners in a single trip on the tieback string so the operator can reduce the overall costs of deployment. Previously, this was not practical because the tieback string weight could not overcome the wellbore friction in horizontal applications.\u0000 In each case, a flotation collar is required to ensure there is enough hook load for deployment of the liner system. The flotation collars used are an interventionless design, utilizing a tempered glass barrier that shatters at a pre-determined applied pressure. The glass debris can be easily circulated through the well without damaging downhole components. This is done commonly on cemented liner and cemented monobore installations, but more rarely with open hole multi-stage completions. For open hole multi-stage completions, the initial installation typically requires an activation tool at the bottom of the well to set the hydraulically activated equipment above.\u0000 Multiple validation tests were completed prior to installation by using an activation tool and flotation collar to ensure the debris could be safely circulated through the internals without closing the activation tool. These activation tools have relatively limited flow area and could cause an issue if the glass debris were to accumulate and shift it closed prematurely. Premature closing of the tool would leave expensive drilling fluids in contact with the reservoir, potentially harming production. For the test, the flotation collar was placed only two pup joints away from the activation tool, resulting in a worst-case scenario where a large amount of debris could potentially encounter the internals of the activation tool at one time. In a downhole environment the flotation collar is typically installed near the build or heel of the well, depending on wellbore geometry. The testing was successfully completed, and the activation tool showed no signs of loading. This resulted in a full-scale trial in the field where a 52 stage, open hole (OH) multi-stage fracturing (MSF) liner was deployed using this technology.\u0000 Through close collaboration with the operator, an acceptable procedure was established to safely circulate the glass debris and further limit the risk of prematurely closing the activation tool. This paper discusses the OH and cemented MSF deployment challenges, detailed lab testing, and field qualification trials for the single trip deployed system. It also highlights operational procedures and best practices when deploying the system in this fashion. A method to calibrate a torque and drag model will also be explored as part of this discussion.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75731071","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}
Reservoir characterization requires accurate prediction of multiple petrophysical properties such as bulk density (or acoustic impedance), porosity, and permeability. However, it remains a big challenge in heterogeneous reservoirs due to significant diagenetic impacts including dissolution, dolomitization, cementation, and fracturing. Most well logs lack the resolution to obtain rock properties in detail in a heterogenous formation. Therefore, it is pertinent to integrate core images into the prediction workflow. This study presents a new approach to solve the problem of obtaining the high-resolution multiple petrophysical properties, by combining machine learning (ML) algorithms and computer vision (CV) techniques. The methodology can be used to automate the process of core data analysis with a minimum number of plugs, thus reducing human effort and cost and improving accuracy. The workflow consists of conditioning and extracting features from core images, correlating well logs and core analysis with those features to build ML models, and applying the models on new cores for petrophysical properties predictions. The core images are preprocessed and analyzed using color models and texture recognition, to extract image characteristics and core textures. The image features are then aggregated into a profile in depth, resampled and aligned with well logs and core analysis. The ML regression models, including classification and regression trees (CART) and deep neural network (DNN), are trained and validated from the filtered training samples of relevant features and target petrophysical properties. The models are then tested on a blind test dataset to evaluate the prediction performance, to predict target petrophysical properties of grain density, porosity and permeability. The profile of histograms of each target property are computed to analyze the data distribution. The feature vectors are extracted from CV analysis of core images and gamma ray logs. The importance of each feature is generated by CART model to individual target, which may be used to reduce model complexity of future model building. The model performances are evaluated and compared on each target. We achieved reasonably good correlation and accuracy on the models, for example, porosity R2=49.7% and RMSE=2.4 p.u., and logarithmic permeability R2=57.8% and RMSE=0.53. The field case demonstrates that inclusion of core image attributes can improve petrophysical regression in heterogenous reservoirs. It can be extended to a multi-well setting to generate vertical distribution of petrophysical properties which can be integrated into reservoir modeling and characterization. Machine leaning algorithms can help automate the workflow and be flexible to be adjusted to take various inputs for prediction.
{"title":"Machine Learning for Multiple Petrophysical Properties Regression Based on Core Images and Well Logs in a Heterogenous Reservoir","authors":"T. Lin, M. Mezghani, Chicheng Xu, Weichang Li","doi":"10.2118/206089-ms","DOIUrl":"https://doi.org/10.2118/206089-ms","url":null,"abstract":"\u0000 Reservoir characterization requires accurate prediction of multiple petrophysical properties such as bulk density (or acoustic impedance), porosity, and permeability. However, it remains a big challenge in heterogeneous reservoirs due to significant diagenetic impacts including dissolution, dolomitization, cementation, and fracturing. Most well logs lack the resolution to obtain rock properties in detail in a heterogenous formation. Therefore, it is pertinent to integrate core images into the prediction workflow.\u0000 This study presents a new approach to solve the problem of obtaining the high-resolution multiple petrophysical properties, by combining machine learning (ML) algorithms and computer vision (CV) techniques. The methodology can be used to automate the process of core data analysis with a minimum number of plugs, thus reducing human effort and cost and improving accuracy. The workflow consists of conditioning and extracting features from core images, correlating well logs and core analysis with those features to build ML models, and applying the models on new cores for petrophysical properties predictions.\u0000 The core images are preprocessed and analyzed using color models and texture recognition, to extract image characteristics and core textures. The image features are then aggregated into a profile in depth, resampled and aligned with well logs and core analysis. The ML regression models, including classification and regression trees (CART) and deep neural network (DNN), are trained and validated from the filtered training samples of relevant features and target petrophysical properties. The models are then tested on a blind test dataset to evaluate the prediction performance, to predict target petrophysical properties of grain density, porosity and permeability. The profile of histograms of each target property are computed to analyze the data distribution. The feature vectors are extracted from CV analysis of core images and gamma ray logs. The importance of each feature is generated by CART model to individual target, which may be used to reduce model complexity of future model building. The model performances are evaluated and compared on each target. We achieved reasonably good correlation and accuracy on the models, for example, porosity R2=49.7% and RMSE=2.4 p.u., and logarithmic permeability R2=57.8% and RMSE=0.53.\u0000 The field case demonstrates that inclusion of core image attributes can improve petrophysical regression in heterogenous reservoirs. It can be extended to a multi-well setting to generate vertical distribution of petrophysical properties which can be integrated into reservoir modeling and characterization. Machine leaning algorithms can help automate the workflow and be flexible to be adjusted to take various inputs for prediction.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"210 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74709582","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}
Z. Fu, Kuan-liang Zhu, Lei Wang, Jing-yi Xu, Qian Wang, Jinzhong Wang, Lingling Wang, Yufei Liu
In oil and gas industry, it is inevitable that the developed reserve will gradually become exhausted. Under such circumstance, in order to stabilize oil production and meet increasing energy demand, we have no choice but to improve oil recovery from matured field as much as possible, since finding new large reservoir is quite hard in the future. For Jidong Oilfield in China, a lot of method can be used for improving oil production, one of which is deep pumping method by increasing pump setting depth, especially for depleted reservoir. Deep pumping method can be helpful to lower bottom hole pressure and enlarge drawdown pressure between producing layer and downhole. Not only can this method generate more power to displace oil from reservoir to well and subsequently increase oil drainage area, leading to higher oil recovery, but also can boost pump fillage and finally obtain high production efficiency. Even though, this method still brings many disadvantages. In Jidong Oilfield, we sometimes set the 1.5in pump at over 3000m depth (in this paper, all well related are rod pumping wells), where varied problems happened as followed:
{"title":"Designing Tools To Improve Rod Pumping Performance In Hostile Production Conditions","authors":"Z. Fu, Kuan-liang Zhu, Lei Wang, Jing-yi Xu, Qian Wang, Jinzhong Wang, Lingling Wang, Yufei Liu","doi":"10.2118/206287-ms","DOIUrl":"https://doi.org/10.2118/206287-ms","url":null,"abstract":"\u0000 In oil and gas industry, it is inevitable that the developed reserve will gradually become exhausted. Under such circumstance, in order to stabilize oil production and meet increasing energy demand, we have no choice but to improve oil recovery from matured field as much as possible, since finding new large reservoir is quite hard in the future. For Jidong Oilfield in China, a lot of method can be used for improving oil production, one of which is deep pumping method by increasing pump setting depth, especially for depleted reservoir.\u0000 Deep pumping method can be helpful to lower bottom hole pressure and enlarge drawdown pressure between producing layer and downhole. Not only can this method generate more power to displace oil from reservoir to well and subsequently increase oil drainage area, leading to higher oil recovery, but also can boost pump fillage and finally obtain high production efficiency. Even though, this method still brings many disadvantages. In Jidong Oilfield, we sometimes set the 1.5in pump at over 3000m depth (in this paper, all well related are rod pumping wells), where varied problems happened as followed:","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76295556","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}
Hydrocarbon field (re-)development requires that a multitude of decisions are made under uncertainty. These decisions include the type and size of surface facilities, location, configuration and number of wells but also which data to acquire. Both types of decisions, which development to choose and which data to acquire, are strongly coupled. The aim of appraisal is to maximize value while minimizing data acquisition costs. These decisions have to be done under uncertainty owing to the inherent uncertainty of the subsurface but also of other costs and economic parameters. Conventional Value Of Information (VOI) evaluations can be used to determine how much can be spend to acquire data. However, VOI is very challenging to calculate for complex sequences of decisions with various costs and including the risk attitude of the decision maker. We are using a fully observable Markov-Decision-Process (MDP) to determine the policy for the sequence and type of measurements and decisions to do. A fully observable MDP is characterised by the states (here: description of the system at a certain point in time), actions (here: measurements and development scenario), transition function (probabilities of transitioning from one state to the next), and rewards (costs for measurements, Expected Monetary Value (EMV) of development options). Solving the MDP gives the optimum policy, sequence of the decisions, the Probability Of Maturation (POM) of a project, the Expected Monetary Value (EMV), the expected loss, the expected appraisal costs, and the Probability of Economic Success (PES). These key performance indicators can then be used to select in a portfolio of projects the ones generating the highest expected reward for the company. Combining the production forecasts from numerical model ensembles with probabilistic capital and operating expenditures and economic parameters allows for quantitative decision making under uncertainty.
{"title":"Hydrocarbon Field Re-Development as Markov Decision Process","authors":"M. Sieberer, T. Clemens","doi":"10.2118/206041-ms","DOIUrl":"https://doi.org/10.2118/206041-ms","url":null,"abstract":"\u0000 Hydrocarbon field (re-)development requires that a multitude of decisions are made under uncertainty. These decisions include the type and size of surface facilities, location, configuration and number of wells but also which data to acquire. Both types of decisions, which development to choose and which data to acquire, are strongly coupled. The aim of appraisal is to maximize value while minimizing data acquisition costs. These decisions have to be done under uncertainty owing to the inherent uncertainty of the subsurface but also of other costs and economic parameters. Conventional Value Of Information (VOI) evaluations can be used to determine how much can be spend to acquire data. However, VOI is very challenging to calculate for complex sequences of decisions with various costs and including the risk attitude of the decision maker.\u0000 We are using a fully observable Markov-Decision-Process (MDP) to determine the policy for the sequence and type of measurements and decisions to do. A fully observable MDP is characterised by the states (here: description of the system at a certain point in time), actions (here: measurements and development scenario), transition function (probabilities of transitioning from one state to the next), and rewards (costs for measurements, Expected Monetary Value (EMV) of development options). Solving the MDP gives the optimum policy, sequence of the decisions, the Probability Of Maturation (POM) of a project, the Expected Monetary Value (EMV), the expected loss, the expected appraisal costs, and the Probability of Economic Success (PES). These key performance indicators can then be used to select in a portfolio of projects the ones generating the highest expected reward for the company. Combining the production forecasts from numerical model ensembles with probabilistic capital and operating expenditures and economic parameters allows for quantitative decision making under uncertainty.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79774783","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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