L. Lim, Henrik Sørensen, Sukit Leekumjorn, A. Pottayil
When it comes to mercury (Hg) there are strict regulation around health, safety and environment, and the level of Hg in discharge water. Further, Hg can potentially compromise the integrity of materials anywhere in the flow path of the produced fluid. Real-time onsite Hg monitoring presents health hazard from exposure to Hg and can also be economically prohibitive. Therefore, it is desirable to be able to reliably simulate Hg partitioning between the vapor, liquid hydrocarbon, and water phases. It is further of interest to evaluate potential Hg condensation when the produced fluid flows from the reservoir through flow lines and passes through process equipment. Commercial compositional reservoir, process and flow simulators employ models with different levels of complexity. It is desirable to be able to make consistent simulations across various simulation platforms using the same equation of state models and model parameters. In this work we present self-contained sets of parameters for use with the original formulations of the Peng-Robinson modification from 1978 and the Soave-Redlich-Kwong equations of state. We aim at using the lowest possible level of complexity of binary interaction parameters. We further give the acentric factors for the original Peng-Robinson equations of state from 1976 giving the same results as when using the Peng-Robinson modification from 1978. The model covers various hydrocarbon components and inorganic gases, H2O, and common hydrate inhibitors. The work is based upon and ties together the experimental and modelling work of others and supplemented with new model parameters where required. We further summarize the accuracy of the model and briefly touch upon how the model extrapolates beyond the limits of data used in this work.
{"title":"A Thermodynamic Model for Prediction of Solubility of Elemental Mercury in Natural Gas, Produced Water and Hydrate Inhibitors","authors":"L. Lim, Henrik Sørensen, Sukit Leekumjorn, A. Pottayil","doi":"10.2118/210631-ms","DOIUrl":"https://doi.org/10.2118/210631-ms","url":null,"abstract":"\u0000 When it comes to mercury (Hg) there are strict regulation around health, safety and environment, and the level of Hg in discharge water. Further, Hg can potentially compromise the integrity of materials anywhere in the flow path of the produced fluid. Real-time onsite Hg monitoring presents health hazard from exposure to Hg and can also be economically prohibitive. Therefore, it is desirable to be able to reliably simulate Hg partitioning between the vapor, liquid hydrocarbon, and water phases. It is further of interest to evaluate potential Hg condensation when the produced fluid flows from the reservoir through flow lines and passes through process equipment. Commercial compositional reservoir, process and flow simulators employ models with different levels of complexity. It is desirable to be able to make consistent simulations across various simulation platforms using the same equation of state models and model parameters.\u0000 In this work we present self-contained sets of parameters for use with the original formulations of the Peng-Robinson modification from 1978 and the Soave-Redlich-Kwong equations of state. We aim at using the lowest possible level of complexity of binary interaction parameters. We further give the acentric factors for the original Peng-Robinson equations of state from 1976 giving the same results as when using the Peng-Robinson modification from 1978. The model covers various hydrocarbon components and inorganic gases, H2O, and common hydrate inhibitors. The work is based upon and ties together the experimental and modelling work of others and supplemented with new model parameters where required. We further summarize the accuracy of the model and briefly touch upon how the model extrapolates beyond the limits of data used in this work.","PeriodicalId":151564,"journal":{"name":"Day 1 Mon, October 17, 2022","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122508654","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}
Colinus Lajim Sayung, Mei Fen Foo, N. Hamza, M. K. Sahrudin, A. Khalid
L-B is a clustered deepwater development comprising two greenfields currently approaching execution stage. The development concept is subsea umbilical, risers and flowlines (SURF) with dedicated Floating Production Storage and Offloading (FPSO) in L field and a long subsea tieback (20km) for B field. Due to this, production assurance is a major risk particularly for B field during production. To enable a holistic simulation of the production and injection system, an integrated network model (INM) is developed. This paper presents the systematic and integrated approach in developing the INM for L-B cluster, the calibration processes, and the resulting field modifications undertaken as an outcome of the model. The INM comprises dynamic reservoir model, well model and network model coupled using an integrator software. Robustness of each standalone model were assured through stringent construction and reviews by respective disciplines. Multiple collaborative forums participated by cross-function members were held to integrate the models. Next, a custom algorithm and method were developed to address specific field controls such as staggered voidage replacement ratio and skin growth over time. Once the models were compatible, multiple scenarios identified from Concept Identification Workshop were evaluated with INM. The results were then integrated into fiscal evaluations and ultimately facilitated decision-making for L-B project. Thorough utilization of the completed INM models generated vital data for future cluster production forecast of L and B fields: The in-situ FPSO operating pressure was accurately simulated using INM resulting in a dynamically responsive production profile, instead of sole dependence on reservoir model which uses a static pressure set up. INM was also used to identify and mitigate potential bottleneck along production system. Preliminary artificial lift options of Electrical Submersible Pump (ESP), Downhole Gaslift (DHG), Subsea Multiphase Pump (MPP) and Riser based Gaslift (RBGL) were analyzed and selectively narrowed down using INM. Outcomes of the analysis were favorable to MPP and RBGL which were then incorporated in the Concept Select scenarios. Ten scenarios with permutations on recovery method, onset of pressure booster installation, and artificial lift requirement were analyzed and decisively selected using results from INM. Study of new technology such as subsea separator was also concluded to be inapplicable in the field via INM evaluation. Finalized temperature modeling was used to cater for flow assurance constraints such as minimum Flowing Tubing Head Temperature (FTHT) requirement and generated inflow information to be incorporated into specialized Flow Assurance (FA) software. This paper will highlight the benefits of a comprehensive integrated network model covering end-to-end operations to mitigate flow assurance risk prior to field start-up. This model will also be readily utilized during the crucial p
{"title":"Integrated Network Modelling for More Robust Production Prediction in Challenging Subsea Deepwater Development","authors":"Colinus Lajim Sayung, Mei Fen Foo, N. Hamza, M. K. Sahrudin, A. Khalid","doi":"10.2118/210643-ms","DOIUrl":"https://doi.org/10.2118/210643-ms","url":null,"abstract":"\u0000 L-B is a clustered deepwater development comprising two greenfields currently approaching execution stage. The development concept is subsea umbilical, risers and flowlines (SURF) with dedicated Floating Production Storage and Offloading (FPSO) in L field and a long subsea tieback (20km) for B field. Due to this, production assurance is a major risk particularly for B field during production. To enable a holistic simulation of the production and injection system, an integrated network model (INM) is developed. This paper presents the systematic and integrated approach in developing the INM for L-B cluster, the calibration processes, and the resulting field modifications undertaken as an outcome of the model.\u0000 The INM comprises dynamic reservoir model, well model and network model coupled using an integrator software. Robustness of each standalone model were assured through stringent construction and reviews by respective disciplines. Multiple collaborative forums participated by cross-function members were held to integrate the models. Next, a custom algorithm and method were developed to address specific field controls such as staggered voidage replacement ratio and skin growth over time. Once the models were compatible, multiple scenarios identified from Concept Identification Workshop were evaluated with INM. The results were then integrated into fiscal evaluations and ultimately facilitated decision-making for L-B project.\u0000 Thorough utilization of the completed INM models generated vital data for future cluster production forecast of L and B fields:\u0000 The in-situ FPSO operating pressure was accurately simulated using INM resulting in a dynamically responsive production profile, instead of sole dependence on reservoir model which uses a static pressure set up. INM was also used to identify and mitigate potential bottleneck along production system. Preliminary artificial lift options of Electrical Submersible Pump (ESP), Downhole Gaslift (DHG), Subsea Multiphase Pump (MPP) and Riser based Gaslift (RBGL) were analyzed and selectively narrowed down using INM. Outcomes of the analysis were favorable to MPP and RBGL which were then incorporated in the Concept Select scenarios. Ten scenarios with permutations on recovery method, onset of pressure booster installation, and artificial lift requirement were analyzed and decisively selected using results from INM. Study of new technology such as subsea separator was also concluded to be inapplicable in the field via INM evaluation. Finalized temperature modeling was used to cater for flow assurance constraints such as minimum Flowing Tubing Head Temperature (FTHT) requirement and generated inflow information to be incorporated into specialized Flow Assurance (FA) software.\u0000 This paper will highlight the benefits of a comprehensive integrated network model covering end-to-end operations to mitigate flow assurance risk prior to field start-up. This model will also be readily utilized during the crucial p","PeriodicalId":151564,"journal":{"name":"Day 1 Mon, October 17, 2022","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130495491","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}
Abolfazl Hashemi, S. Borazjani, Cuong Nguyen, Grace Loi, A. Badalyan, B. Dang-Le, P. Bedrikovetsky
Fines detachment is an important component of methane production from Coal Bed Methane reservoirs. Production of coal fines is widely observed during dewatering and simultaneous gas-water production. The theory for fines detachment by drag against electrostatic attraction, model of the transport of those detrital fines, and their validation by laboratory test is widely used for planning and design of Coal Seam Gas developments. However, clay particles that naturally grow on coal grains and asperous parts of coal surfaces (authigenic and potential coal fines) are detached by breakage. To the best of our knowledge, the analytical theory for detachment of authigenic and potential coal fines is not available. The present paper fills the gap. Based on Timoshenko's beam theory, we derive failure conditions for breakage of authigenic and potential coal fines of the rock surface. It allows defining maximum retention function for fines breakage. The maximum retention is incorporated into transport equation of mobilized fines, allowing developing analytical models for linear flow of core flooding and radial flow of well inflow performance. Matching of laboratory coreflood data from four laboratory studies show high agreement. The model coefficients obtained by treatment of laboratory data allow predicting skin growth in production wells under fines migration.
{"title":"Fines Migration and Production in CSG Reservoirs: Laboratory & Modelling Study","authors":"Abolfazl Hashemi, S. Borazjani, Cuong Nguyen, Grace Loi, A. Badalyan, B. Dang-Le, P. Bedrikovetsky","doi":"10.2118/210764-ms","DOIUrl":"https://doi.org/10.2118/210764-ms","url":null,"abstract":"\u0000 Fines detachment is an important component of methane production from Coal Bed Methane reservoirs. Production of coal fines is widely observed during dewatering and simultaneous gas-water production. The theory for fines detachment by drag against electrostatic attraction, model of the transport of those detrital fines, and their validation by laboratory test is widely used for planning and design of Coal Seam Gas developments. However, clay particles that naturally grow on coal grains and asperous parts of coal surfaces (authigenic and potential coal fines) are detached by breakage. To the best of our knowledge, the analytical theory for detachment of authigenic and potential coal fines is not available. The present paper fills the gap. Based on Timoshenko's beam theory, we derive failure conditions for breakage of authigenic and potential coal fines of the rock surface. It allows defining maximum retention function for fines breakage. The maximum retention is incorporated into transport equation of mobilized fines, allowing developing analytical models for linear flow of core flooding and radial flow of well inflow performance. Matching of laboratory coreflood data from four laboratory studies show high agreement. The model coefficients obtained by treatment of laboratory data allow predicting skin growth in production wells under fines migration.","PeriodicalId":151564,"journal":{"name":"Day 1 Mon, October 17, 2022","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134339879","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}
Erfan Saber, Q. Qu, S. Aminossadati, Zhongwei Chen
Horizontal boreholes have been routinely applied to coal seams as a cost-effective way to maximize coal seam gas production. However, these wells can encounter severe instability issues during field development due to significant horizontal stress loss and change in deviatoric stresses acting on the borehole. In this work, a general dual-porosity dual-permeability model is established and assigned to a coupled gas flow and coal deformation numerical model to investigate permeability change and borehole break-out regarding different in-situ stress regimes around a horizontal borehole. Mohr-Coulomb failure criterion is used in this model. The results show that drilling parallel to the maximum horizontal stress direction neither achieves the best stability of the borehole nor maximizes the permeability ratio. Drilling along the minimum horizontal stress direction would maximize the permeability ratio, but it has the worst stability. The optimal drilling direction window considering both permeability ratio and borehole stability is recommended to be between 45– 60°.
{"title":"Horizontal Coal Seam Gas Well Orientation Optimization: The Impact of Stress Regime","authors":"Erfan Saber, Q. Qu, S. Aminossadati, Zhongwei Chen","doi":"10.2118/210647-ms","DOIUrl":"https://doi.org/10.2118/210647-ms","url":null,"abstract":"\u0000 Horizontal boreholes have been routinely applied to coal seams as a cost-effective way to maximize coal seam gas production. However, these wells can encounter severe instability issues during field development due to significant horizontal stress loss and change in deviatoric stresses acting on the borehole. In this work, a general dual-porosity dual-permeability model is established and assigned to a coupled gas flow and coal deformation numerical model to investigate permeability change and borehole break-out regarding different in-situ stress regimes around a horizontal borehole. Mohr-Coulomb failure criterion is used in this model.\u0000 The results show that drilling parallel to the maximum horizontal stress direction neither achieves the best stability of the borehole nor maximizes the permeability ratio. Drilling along the minimum horizontal stress direction would maximize the permeability ratio, but it has the worst stability. The optimal drilling direction window considering both permeability ratio and borehole stability is recommended to be between 45– 60°.","PeriodicalId":151564,"journal":{"name":"Day 1 Mon, October 17, 2022","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131116087","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 consistent leading cause of drilling non-productive time (NPT) is the inability to steer the planned well trajectory trouble-free. Separate from downhole tool and drill bit failures, an unplanned trip to change the Bottom Hole Assembly (BHA) is required for up to one in every seven drilling runs. Root cause analyses indicate potentially a quarter of all drilling NPT has poor planning or field execution as the failure mechanism, signifying scope for improvement. This paper aims to help guide optimal selection of RSS/motor and bit, to ensure challenging wellpaths will be achieved with minimal NPT associated with BHA trips. Directional drilling analysis typically compares dogleg severity (DLS) for planned and actual trajectory. This metric is fundamentally direction-blind; absolute tortuosity is represented whether planned or unintentional. Without full context, DLS analysis can mask many steering issues. Typically, industry software does not measure how closely the steering inputs match their anticipated responses during a run. Strategic management and identification of zones with erratic toolface control, or strong formation/BHA tendencies is critical. The proposed ‘derived steering’ analytics method was applied to plan demanding 3D trajectories for an Extended Reach offshore campaign in Australia. Existing minimum curvature equations were repurposed to plot previous runs steering inputs and then infer efficiencies for each formation. Supervision was essential to counteract strong consistent right-hand BHA walk tendency for all the variety of wells studied. Multiple NPT events on previous campaigns had resulted from poor steering response in the shallow interbedded geology. In view of quantifiable field-specific risks, wellplans were refined to minimize tortuosity and maximize the design safety factor. The combination of highest anticipated dogleg response rotary steerable technology and bit selection was selected for steering assurance. Modelled tendencies per lithology were shared with wellsite supervisors, and recent drilling results essentially mimicked data analytics. Others operating in this field in the 21st century had drilled total meterage of 36,740m MD from 83 runs. Bit Gradings showed two ‘Lost in Holes’, one ‘Drill String Failure’, six trips for ‘Downhole Tool Failures’, seven for ‘Penetration Rate’, six to ‘Change BHA’, two for ‘Hole Problems’ and one for ‘Downhole Motor Failure’. The current campaign's improved directional drilling offset analysis contributed towards significant avoidance of well delivery NPT to drill 28,061m in 34 runs. No trips were required to change BHA or bit because of inability to follow the trajectory, and field teams were able to pre-empt lithology-specific challenges.
{"title":"Alleviating Directional Well Trajectory Problems via Data Analytics","authors":"L. Clayton, Ming Hwa Lee, A. Salmachi","doi":"10.2118/210766-ms","DOIUrl":"https://doi.org/10.2118/210766-ms","url":null,"abstract":"\u0000 A consistent leading cause of drilling non-productive time (NPT) is the inability to steer the planned well trajectory trouble-free. Separate from downhole tool and drill bit failures, an unplanned trip to change the Bottom Hole Assembly (BHA) is required for up to one in every seven drilling runs. Root cause analyses indicate potentially a quarter of all drilling NPT has poor planning or field execution as the failure mechanism, signifying scope for improvement. This paper aims to help guide optimal selection of RSS/motor and bit, to ensure challenging wellpaths will be achieved with minimal NPT associated with BHA trips.\u0000 Directional drilling analysis typically compares dogleg severity (DLS) for planned and actual trajectory. This metric is fundamentally direction-blind; absolute tortuosity is represented whether planned or unintentional. Without full context, DLS analysis can mask many steering issues. Typically, industry software does not measure how closely the steering inputs match their anticipated responses during a run. Strategic management and identification of zones with erratic toolface control, or strong formation/BHA tendencies is critical.\u0000 The proposed ‘derived steering’ analytics method was applied to plan demanding 3D trajectories for an Extended Reach offshore campaign in Australia. Existing minimum curvature equations were repurposed to plot previous runs steering inputs and then infer efficiencies for each formation. Supervision was essential to counteract strong consistent right-hand BHA walk tendency for all the variety of wells studied. Multiple NPT events on previous campaigns had resulted from poor steering response in the shallow interbedded geology.\u0000 In view of quantifiable field-specific risks, wellplans were refined to minimize tortuosity and maximize the design safety factor. The combination of highest anticipated dogleg response rotary steerable technology and bit selection was selected for steering assurance. Modelled tendencies per lithology were shared with wellsite supervisors, and recent drilling results essentially mimicked data analytics.\u0000 Others operating in this field in the 21st century had drilled total meterage of 36,740m MD from 83 runs. Bit Gradings showed two ‘Lost in Holes’, one ‘Drill String Failure’, six trips for ‘Downhole Tool Failures’, seven for ‘Penetration Rate’, six to ‘Change BHA’, two for ‘Hole Problems’ and one for ‘Downhole Motor Failure’. The current campaign's improved directional drilling offset analysis contributed towards significant avoidance of well delivery NPT to drill 28,061m in 34 runs. No trips were required to change BHA or bit because of inability to follow the trajectory, and field teams were able to pre-empt lithology-specific challenges.","PeriodicalId":151564,"journal":{"name":"Day 1 Mon, October 17, 2022","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129263641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Z. Mohd Sahak, Maung Maung Myo Thant, Tengku Amansyah Tuan Mat, Eugene Castillano, Abhijeet Bhave
For oilfield with high associated gas production or non-associated gas (NAG) wells, gas injection is typically being given as priority, and only the resulting gas flow not taken by injection wells is exported as sales gas. With the unstable price of oil and the shift towards gas as cleaner energy source thus creating higher gas demand, the produced gas maybe prioritized for sales rather than for injection. This paper demonstrates the new approach of managing the gas export and gas injection including the flexibility in prioritizing either utilization options, and managing the impact of changes in injection gas supply accordingly via process analytics and automation. The paper described the concept and key design consideration in integrating the gas injection and gas export process analytics and control in oil and gas fields for improved hydrocarbon recovery application and flexibility of operation modes. It also described step-by-step approach for the technology development and adoption, which is a commendable to be replicated for other production system. Based on a case study, current operation gaps, limitation and opportunity are identified from system review, followed by development of automation strategy, mainly focusing at utilizing the current instrumentations available at the field to manage the gas export and injection accordingly based on desired prioritized mode. With the automation exercise, the operator can now control the system by changing the priority mode and set points at DCS rather than manually adjusting the choke valves opening to regulate the gas injection and gas export flow.
{"title":"Unified Gas Injection and Gas Export Management via Process Automation","authors":"M. Z. Mohd Sahak, Maung Maung Myo Thant, Tengku Amansyah Tuan Mat, Eugene Castillano, Abhijeet Bhave","doi":"10.2118/210615-ms","DOIUrl":"https://doi.org/10.2118/210615-ms","url":null,"abstract":"\u0000 For oilfield with high associated gas production or non-associated gas (NAG) wells, gas injection is typically being given as priority, and only the resulting gas flow not taken by injection wells is exported as sales gas. With the unstable price of oil and the shift towards gas as cleaner energy source thus creating higher gas demand, the produced gas maybe prioritized for sales rather than for injection. This paper demonstrates the new approach of managing the gas export and gas injection including the flexibility in prioritizing either utilization options, and managing the impact of changes in injection gas supply accordingly via process analytics and automation. The paper described the concept and key design consideration in integrating the gas injection and gas export process analytics and control in oil and gas fields for improved hydrocarbon recovery application and flexibility of operation modes. It also described step-by-step approach for the technology development and adoption, which is a commendable to be replicated for other production system. Based on a case study, current operation gaps, limitation and opportunity are identified from system review, followed by development of automation strategy, mainly focusing at utilizing the current instrumentations available at the field to manage the gas export and injection accordingly based on desired prioritized mode. With the automation exercise, the operator can now control the system by changing the priority mode and set points at DCS rather than manually adjusting the choke valves opening to regulate the gas injection and gas export flow.","PeriodicalId":151564,"journal":{"name":"Day 1 Mon, October 17, 2022","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127628162","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}
Nur Dalila Alias, Bak Shiiun Wong, Wan Zalikha Anas, Nur Amalina Sulaiman, Mildred Vanessa Epui, Azam A Rahman, A. R. A Rahman, Sue Jane Yeoh, A. Abdollahzadeh, Linda William Ngadan, Horng Eng Tang, Wai Fun Chooi, R. Khan, Sook Moi Ng, S. N. Saminal, M. Ibrahim, Marklin Hamid, A. S. Suhaili, M. S. F. M Hisham
Leveraged on the abundant weight data comprised of more than 200 offshore platforms, a smart digitalized analytical tool called i-WEIGHT, an integrated weight control tool consisting of three (3) main modules: centralized multi-discipline weight database module for all offshore platforms, seamlessly linked with Insights dashboard module in providing actionable insights, and weight predictive module supported by Machine Learning (ML) model was developed. This paper discussed the Minimum Viable Product (MVP) Phase 1 development outcome, using a close-loop weight control ecosystem for continuous update of validated weight data in Module 1, and eventually improve & enhance capability of both the EDA and Predictive module. Using a supervised machine learning algorithms, the identified target variables were observed to provide weight prediction between 16% to 38% of Mean Absolute Percentage Error (MAPE), using Extreme Gradient Boosting Regressor (XGBR) algorithm. Top 10 important features were identified for each target variable, which provide insights to the operators on critical data required for topside with identified missing equipment weight data for future i-WEIGHT improvement. Based on more than 200 integrated platform topside data gathered for this study, consolidated insights from the data enabled operators to identify the threat of current data quality and thus bringing forward a promising opportunity to enhance platform weight data management system. Having a centralized and automated platform weights data, this tool has the potential answers for United Nations’ Sustainability Development Goals, in particular Goal 9.4, where the study represents a small but crucial step to upgrade from an existing conventional process into a digitally driven operation, introducing a sustainable ecosystem in offshore structure weight management, thus fostering sustainable growth within the industry.
{"title":"Digital Transformation of Offshore Structure Weight Control Management into Digitally Integrated and Intelligent Analytical Tool","authors":"Nur Dalila Alias, Bak Shiiun Wong, Wan Zalikha Anas, Nur Amalina Sulaiman, Mildred Vanessa Epui, Azam A Rahman, A. R. A Rahman, Sue Jane Yeoh, A. Abdollahzadeh, Linda William Ngadan, Horng Eng Tang, Wai Fun Chooi, R. Khan, Sook Moi Ng, S. N. Saminal, M. Ibrahim, Marklin Hamid, A. S. Suhaili, M. S. F. M Hisham","doi":"10.2118/210712-ms","DOIUrl":"https://doi.org/10.2118/210712-ms","url":null,"abstract":"\u0000 Leveraged on the abundant weight data comprised of more than 200 offshore platforms, a smart digitalized analytical tool called i-WEIGHT, an integrated weight control tool consisting of three (3) main modules: centralized multi-discipline weight database module for all offshore platforms, seamlessly linked with Insights dashboard module in providing actionable insights, and weight predictive module supported by Machine Learning (ML) model was developed. This paper discussed the Minimum Viable Product (MVP) Phase 1 development outcome, using a close-loop weight control ecosystem for continuous update of validated weight data in Module 1, and eventually improve & enhance capability of both the EDA and Predictive module. Using a supervised machine learning algorithms, the identified target variables were observed to provide weight prediction between 16% to 38% of Mean Absolute Percentage Error (MAPE), using Extreme Gradient Boosting Regressor (XGBR) algorithm. Top 10 important features were identified for each target variable, which provide insights to the operators on critical data required for topside with identified missing equipment weight data for future i-WEIGHT improvement. Based on more than 200 integrated platform topside data gathered for this study, consolidated insights from the data enabled operators to identify the threat of current data quality and thus bringing forward a promising opportunity to enhance platform weight data management system. Having a centralized and automated platform weights data, this tool has the potential answers for United Nations’ Sustainability Development Goals, in particular Goal 9.4, where the study represents a small but crucial step to upgrade from an existing conventional process into a digitally driven operation, introducing a sustainable ecosystem in offshore structure weight management, thus fostering sustainable growth within the industry.","PeriodicalId":151564,"journal":{"name":"Day 1 Mon, October 17, 2022","volume":"299 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114579166","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}
Nur Hazrina Kamarul Zaman, Z. Johar, I. H. A Salam, M. K. Sahrudin, M. R. A Raub, Mei Fen Foo
The paper discusses on reservoir souring study in a deep water subsea green field as a result of seawater injection. The objectives are to determine likelihood, timing of reservoir souring to happen and amount of expected produced H2S. Offshore deep water development involves considerable CAPEX investment hence reservoir souring requires to be assessed in order to make techno-commercial judgement involving formulating the field development plan, upfront identification of prevention & mitigation strategy, operating strategy and project economics. The study started by performing data gathering involving among others field information, PVT, mineralogy, water analysis data, and production and injection profile. Subsequently, 2D reservoir modelling and 3D reservoir modelling was built. Sensitivities cases were run by varying the injection rate, nutrient loading, rock abstraction capacity, sulphate content, injection temperature and bacteria growth time. This is followed by sensitivity analyses for mitigation options using biocide injection, nitrate injection, H2S scavenging and sulphate removal in the field. Based on the results obtained, prevention and mitigation strategy has been evaluated and ranked followed by comparison with nearby analogue fields. The modelling results of all scenarios indicate that reservoir souring will happen in the field and beyond HSE safety limit. For some scenarios, the H2S partial pressure exceeds NACE limit before end of field life, hence requiring team to re-evaluate material selection options. Water injection rate and rock abstraction capacity have the largest impact to the H2S breakthrough time. Sensitivity analyses for mitigation options have been conducted based on consideration of having options of biocide injection, nitrate injection, H2S scavenging and sulphate removal in the field. Biocide injection does not have considerable effects on H2S level. Nitrate injection only partially reduces H2S generation mainly due to high nutrient content in the reservoir and high sulphate content in the injected seawater. On the other hand, sulphate removal analyses indicate its effectiveness in preventing reservoir from becoming sour. The outcome of the study is then incorporated in the field development plan and operating strategy. The paper highlighted comprehensive step by step approach to understand reservoir souring potential in a deep water development via 2D and 3D modelling approach. This can be included as an important procedure in field development especially involving high CAPEX development whereby critical decision making need to be made upfront. In addition, benchmarking, and learnings from nearby deep water fields help to identify best preventive and remedial option for reservoir souring.
{"title":"Reservoir Souring Study for De-Risking A Deep Water Subsea Green Field Development","authors":"Nur Hazrina Kamarul Zaman, Z. Johar, I. H. A Salam, M. K. Sahrudin, M. R. A Raub, Mei Fen Foo","doi":"10.2118/210761-ms","DOIUrl":"https://doi.org/10.2118/210761-ms","url":null,"abstract":"\u0000 The paper discusses on reservoir souring study in a deep water subsea green field as a result of seawater injection. The objectives are to determine likelihood, timing of reservoir souring to happen and amount of expected produced H2S. Offshore deep water development involves considerable CAPEX investment hence reservoir souring requires to be assessed in order to make techno-commercial judgement involving formulating the field development plan, upfront identification of prevention & mitigation strategy, operating strategy and project economics.\u0000 The study started by performing data gathering involving among others field information, PVT, mineralogy, water analysis data, and production and injection profile. Subsequently, 2D reservoir modelling and 3D reservoir modelling was built. Sensitivities cases were run by varying the injection rate, nutrient loading, rock abstraction capacity, sulphate content, injection temperature and bacteria growth time. This is followed by sensitivity analyses for mitigation options using biocide injection, nitrate injection, H2S scavenging and sulphate removal in the field. Based on the results obtained, prevention and mitigation strategy has been evaluated and ranked followed by comparison with nearby analogue fields.\u0000 The modelling results of all scenarios indicate that reservoir souring will happen in the field and beyond HSE safety limit. For some scenarios, the H2S partial pressure exceeds NACE limit before end of field life, hence requiring team to re-evaluate material selection options. Water injection rate and rock abstraction capacity have the largest impact to the H2S breakthrough time. Sensitivity analyses for mitigation options have been conducted based on consideration of having options of biocide injection, nitrate injection, H2S scavenging and sulphate removal in the field. Biocide injection does not have considerable effects on H2S level. Nitrate injection only partially reduces H2S generation mainly due to high nutrient content in the reservoir and high sulphate content in the injected seawater. On the other hand, sulphate removal analyses indicate its effectiveness in preventing reservoir from becoming sour. The outcome of the study is then incorporated in the field development plan and operating strategy.\u0000 The paper highlighted comprehensive step by step approach to understand reservoir souring potential in a deep water development via 2D and 3D modelling approach. This can be included as an important procedure in field development especially involving high CAPEX development whereby critical decision making need to be made upfront. In addition, benchmarking, and learnings from nearby deep water fields help to identify best preventive and remedial option for reservoir souring.","PeriodicalId":151564,"journal":{"name":"Day 1 Mon, October 17, 2022","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128529823","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. Jordan, R. Koochak, Martin Roberts, A. Nalonnil, Mike Honeychurch
Analyses have been widely applied in production forecasting of oil/gas production in both conventional and unconventional reservoirs. In order to forecast production, traditional regression and machine learning approaches have been applied to various reservoir analysis methods. Nevertheless, these methods are still suboptimal in detecting similar production trends in different wells due to data artifacts (noise, data scatter, outliers) that obscure the reservoir signal and leading to large forecast error, or fail due to lack of data access (inadequate SCADA systems, missing or abhorrent data, and much more). Furthermore, without proper and complete integration into a data system, discipline silos still exist reducing the efficiency of automation. This paper describes a recent field trial conducted in Australia's Cooper Basin with the objective to develop a completely automated end-to-end system in which data are captured directly from the field/SCADA system, automatically imported/processed, and finally analyzed entirely in automated system using modern computing languages, modern devices incl. IoT, as well as advanced data science and machine learning methods. This was a multidisciplinary undertaking requiring expertise from petroleum, computing/programming, and data science disciplines. The back-end layer was developed using Wolfram's computation engine, run from an independent server in Australia, while the front-end graphical user interface (GUI) was developed using a combination of Wolfram Language, Java, and JavaScript – all later switched to a Python-React combination after extensive testing. The system was designed to simultaneously capture data real-time from SCADA Historians, IIoT devices, and remote databases for automatic processing and analysis through API's. Automatic processing included "Smart Filtering" using apparent Productivity Index and similar methods. Automated analysis, including scenario analysis, was performed using customized M/L and statistical methods which are then applied to Decline curve analysis (DCA), flowing material balance analysis (FMB), and Water-Oil-Ratio (WOR). The entire procedure is automated, without need for any human intervention.
{"title":"A Holistic Approach to Big Data and Data Analytics for Automated Reservoir Surveillance and Analysis","authors":"C. Jordan, R. Koochak, Martin Roberts, A. Nalonnil, Mike Honeychurch","doi":"10.2118/210757-ms","DOIUrl":"https://doi.org/10.2118/210757-ms","url":null,"abstract":"\u0000 Analyses have been widely applied in production forecasting of oil/gas production in both conventional and unconventional reservoirs. In order to forecast production, traditional regression and machine learning approaches have been applied to various reservoir analysis methods. Nevertheless, these methods are still suboptimal in detecting similar production trends in different wells due to data artifacts (noise, data scatter, outliers) that obscure the reservoir signal and leading to large forecast error, or fail due to lack of data access (inadequate SCADA systems, missing or abhorrent data, and much more). Furthermore, without proper and complete integration into a data system, discipline silos still exist reducing the efficiency of automation.\u0000 This paper describes a recent field trial conducted in Australia's Cooper Basin with the objective to develop a completely automated end-to-end system in which data are captured directly from the field/SCADA system, automatically imported/processed, and finally analyzed entirely in automated system using modern computing languages, modern devices incl. IoT, as well as advanced data science and machine learning methods. This was a multidisciplinary undertaking requiring expertise from petroleum, computing/programming, and data science disciplines.\u0000 The back-end layer was developed using Wolfram's computation engine, run from an independent server in Australia, while the front-end graphical user interface (GUI) was developed using a combination of Wolfram Language, Java, and JavaScript – all later switched to a Python-React combination after extensive testing. The system was designed to simultaneously capture data real-time from SCADA Historians, IIoT devices, and remote databases for automatic processing and analysis through API's. Automatic processing included \"Smart Filtering\" using apparent Productivity Index and similar methods. Automated analysis, including scenario analysis, was performed using customized M/L and statistical methods which are then applied to Decline curve analysis (DCA), flowing material balance analysis (FMB), and Water-Oil-Ratio (WOR). The entire procedure is automated, without need for any human intervention.","PeriodicalId":151564,"journal":{"name":"Day 1 Mon, October 17, 2022","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124559717","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 intrinsic heterogeneous nature of Coal Seam Gas (CSG) reservoirs can significantly diminish the expected return. This paper presents a new method to decrease inherent investment risk by delineating permeability and provides illustrative case studies. The approach identified riskier reservoir areas to effectively place delineation wells and optimise the number of fracture wells, all with an aim to reach economic gas rates with minimum expenditure and maximum value during development phases. This work characterises high-mid-low permeability and coalbed gas saturation areas through a novel methodology that uses normalised production rates and net coal measurements. As a result, a comprehensive risk map is generated, from which new development locations near high-risk areas are proposed for permeability delineation. The data acquired from delineation defines which development wells need to be fractured to minimise risk. Additionally, the effect of dewatering on gas production is investigated, along with the impact of fractured wells on mitigating risk. The approach also incorporates a decision trees analysis to predict incremental value resulting from applying the new approach. A reliable qualitative characterisation of permeability and coalbed gas saturation was obtained after using normalised production rates and net coal measurements in a particular CSG reservoir. Probabilistic distributions of actual production trends show a detrimental effect of low permeability in dewatering and, subsequently, gas recovery. This condition worsens as gas saturation decreases. The above findings are crucial to categorising risk and generating a risk map. An effective delineation well placement is determined based on the existing high-risk area's size and location. Overall, this methodology provides an effective placement of delineation and fracture wells to identify and mitigate risk, respectively. According to economic risk assessment in an illustrative case, the expected return is projected to increase by more than five times from implementing the new delineation approach. This approach fits the current industry needs very well, as it is reliable, maximises return and can be easily integrated into the development strategy of any CSG reservoir. The novelty of the methodology relies on the qualitative identification of permeability and gas saturation to capture reservoir heterogeneity and categorise risk, all to optimise delineation and fractured wells placement. It can be applied to reservoirs where heterogeneity characterisation through traditional tools is not economically feasible.
{"title":"New Approach for Reducing Investment Risk Through the Development of a Permeability Delineation Strategy in Coal Seam Gas Reservoirs","authors":"Diana Paola Olarte Caro","doi":"10.2118/210661-ms","DOIUrl":"https://doi.org/10.2118/210661-ms","url":null,"abstract":"\u0000 The intrinsic heterogeneous nature of Coal Seam Gas (CSG) reservoirs can significantly diminish the expected return. This paper presents a new method to decrease inherent investment risk by delineating permeability and provides illustrative case studies. The approach identified riskier reservoir areas to effectively place delineation wells and optimise the number of fracture wells, all with an aim to reach economic gas rates with minimum expenditure and maximum value during development phases.\u0000 This work characterises high-mid-low permeability and coalbed gas saturation areas through a novel methodology that uses normalised production rates and net coal measurements. As a result, a comprehensive risk map is generated, from which new development locations near high-risk areas are proposed for permeability delineation. The data acquired from delineation defines which development wells need to be fractured to minimise risk. Additionally, the effect of dewatering on gas production is investigated, along with the impact of fractured wells on mitigating risk. The approach also incorporates a decision trees analysis to predict incremental value resulting from applying the new approach.\u0000 A reliable qualitative characterisation of permeability and coalbed gas saturation was obtained after using normalised production rates and net coal measurements in a particular CSG reservoir. Probabilistic distributions of actual production trends show a detrimental effect of low permeability in dewatering and, subsequently, gas recovery. This condition worsens as gas saturation decreases. The above findings are crucial to categorising risk and generating a risk map. An effective delineation well placement is determined based on the existing high-risk area's size and location. Overall, this methodology provides an effective placement of delineation and fracture wells to identify and mitigate risk, respectively. According to economic risk assessment in an illustrative case, the expected return is projected to increase by more than five times from implementing the new delineation approach.\u0000 This approach fits the current industry needs very well, as it is reliable, maximises return and can be easily integrated into the development strategy of any CSG reservoir. The novelty of the methodology relies on the qualitative identification of permeability and gas saturation to capture reservoir heterogeneity and categorise risk, all to optimise delineation and fractured wells placement. It can be applied to reservoirs where heterogeneity characterisation through traditional tools is not economically feasible.","PeriodicalId":151564,"journal":{"name":"Day 1 Mon, October 17, 2022","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123302109","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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