Ahmed S. Rizk, Moussa Tembely, W. Alameri, E. Al-Shalabi
Increasing global oil demand, combined with limited new discoveries, compels oil companies to maximize the value of existing resources by employing enhanced oil recovery (EOR) techniques aimed at the remaining oil. Estimating residual oil saturation (Sor) in the reservoir after conventional recovery techniques, such as waterflooding is critical in screening the suitable EOR technique and in further field development and production prediction. The objective of this work is to provide an artificial intelligence (AI) workflow to assess Sor of carbonate rocks, which will aid in the development of a long-term strategy for efficient production in this fourth industrial age. In the present work, two-phase lattice Boltzmann method (LBM) simulation was used with the benefit of high parallelization schemes. After applying the CPU-based solver using LBM on thousands of carbonate rock digital images, an AI-based workflow was developed to estimate Sor. Different advanced tree-based regression models were tested. Relevant input features were extracted from complex carbonate micro-CT images including porosity, absolute permeability, pore size and pore-throat size distributions, as well as rock surface roughness distribution. These features were fed into the learning models as inputs; while the output used to train and test the models is based on the direct simulation results of Sor from the image dataset. The results showed that extracting the engineered features from images aided in building a physics-informed machine learning model (ML) capable of accurately predicting Sor of carbonate rocks from their dry images. Three ML models were trained and tested on more than 1000 data points, namely gradient boosting, random forest, and xgradient boosting. Even with such small number of data points, the three models yielded promising results. Gradient boosting algorithm showed the highest predictive capability among the three techniques, with an R2 of 0.71. Increasing the number of data points is expected to help the models capture wider ranges of rock properties, and consequently, result in an increase in the prediction capability of the models. To the best of our knowledge, this is the first study that leverages machine learning to estimating residual oil saturation in complex carbonate. This work will contribute to the development of a novel framework for estimating accurately and reliably residual oil saturation of heterogeneous rocks. As a result, this research will aid in providing decision-makers with a simple tool for screening the most suitable EOR technique for optimal asset use.
{"title":"Residual Oil Saturation Estimation from Carbonate Rock Images Based on Direct Simulation and Machine Learning","authors":"Ahmed S. Rizk, Moussa Tembely, W. Alameri, E. Al-Shalabi","doi":"10.2523/iptc-22096-ms","DOIUrl":"https://doi.org/10.2523/iptc-22096-ms","url":null,"abstract":"\u0000 Increasing global oil demand, combined with limited new discoveries, compels oil companies to maximize the value of existing resources by employing enhanced oil recovery (EOR) techniques aimed at the remaining oil. Estimating residual oil saturation (Sor) in the reservoir after conventional recovery techniques, such as waterflooding is critical in screening the suitable EOR technique and in further field development and production prediction. The objective of this work is to provide an artificial intelligence (AI) workflow to assess Sor of carbonate rocks, which will aid in the development of a long-term strategy for efficient production in this fourth industrial age. In the present work, two-phase lattice Boltzmann method (LBM) simulation was used with the benefit of high parallelization schemes. After applying the CPU-based solver using LBM on thousands of carbonate rock digital images, an AI-based workflow was developed to estimate Sor. Different advanced tree-based regression models were tested. Relevant input features were extracted from complex carbonate micro-CT images including porosity, absolute permeability, pore size and pore-throat size distributions, as well as rock surface roughness distribution. These features were fed into the learning models as inputs; while the output used to train and test the models is based on the direct simulation results of Sor from the image dataset.\u0000 The results showed that extracting the engineered features from images aided in building a physics-informed machine learning model (ML) capable of accurately predicting Sor of carbonate rocks from their dry images. Three ML models were trained and tested on more than 1000 data points, namely gradient boosting, random forest, and xgradient boosting. Even with such small number of data points, the three models yielded promising results. Gradient boosting algorithm showed the highest predictive capability among the three techniques, with an R2 of 0.71. Increasing the number of data points is expected to help the models capture wider ranges of rock properties, and consequently, result in an increase in the prediction capability of the models. To the best of our knowledge, this is the first study that leverages machine learning to estimating residual oil saturation in complex carbonate. This work will contribute to the development of a novel framework for estimating accurately and reliably residual oil saturation of heterogeneous rocks. As a result, this research will aid in providing decision-makers with a simple tool for screening the most suitable EOR technique for optimal asset use.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84872485","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. Popeney, Kellen Harkness, Laura Copeland, Jesse Lee, Dmitry Usoltsev
The presence of extensive tight oil reserves in regions with scarce or intermittent supply of ground or surface freshwater resources underscores the importance of water reduction, reuse, and recycling strategies to ensure the sustainability of hydraulic fracturing. Currently, the advantages of using synthetic high viscosity friction reducers (HVFRs) does not extend to fluids composed of high salinity produced water or wastewater because these products cannot transport proppant effectively under such conditions. The use of a new fully synthetic polymer architecture bearing interchain association is described which provides a significant increase in brine tolerance and fluid rheology, giving rise to effective proppant transport and friction reduction in water salinity exceeding 200,000 TDS. Two variations of the polymer architecture are described: a product that operates effectively in fluid below 100,000 TDS such as seawater (SW-HVFR) and a second high brine product that works in all fluids up to and above 200,000 TDS (HB-HVFR). Proppant transport was studied dynamically using a slot flow apparatus, which demonstrated performance with the new system that exceeded guar and greatly exceeded other conventional HVFRs at equivalent polymer loadings. Slot flow results indicated consistent transport performance throughout the salinity range investigated by proper selection of SW-HVFR and HB-HVFR. Although high shear viscosity of the new polymers was inferior to that of guar, advanced rheological studiesindicated that the superior performance was due to enhanced viscosity and unusually high elasticity of the derived fluids within the relevant shear rate range between 1 and 100 s−1. In addition, anomalous dependence of viscosity on temperature is described, featuring a viscosity maximum above ambient temperature. This unusual rheology behavior was attributed to the associative polymer architecture of the new system. The new HVFRs exhibit effective friction reduction within their intended salinity ranges as well as good tolerance toward biocides and clay control agents. Furthermore, the operational salinity range of SW-HVFR can be extended up to 200,000 TDS in the presence of certain production enhancement aids due to a synergistic effect on the polymer dissolution rate. Lastly, bottle testing indicated that the polymers are effectively broken by common oxidative breakers, enabling their flowback. These results demonstrate the flexibility of the new HVFR system to make total fluids utilizing any water source, enabling sustainable fracturing in a variety of situations.
{"title":"Revamping Polymer Architecture for Optimized Fracturing Fluids in Fresh and Produced Water","authors":"C. Popeney, Kellen Harkness, Laura Copeland, Jesse Lee, Dmitry Usoltsev","doi":"10.2523/iptc-22434-ms","DOIUrl":"https://doi.org/10.2523/iptc-22434-ms","url":null,"abstract":"\u0000 The presence of extensive tight oil reserves in regions with scarce or intermittent supply of ground or surface freshwater resources underscores the importance of water reduction, reuse, and recycling strategies to ensure the sustainability of hydraulic fracturing. Currently, the advantages of using synthetic high viscosity friction reducers (HVFRs) does not extend to fluids composed of high salinity produced water or wastewater because these products cannot transport proppant effectively under such conditions. The use of a new fully synthetic polymer architecture bearing interchain association is described which provides a significant increase in brine tolerance and fluid rheology, giving rise to effective proppant transport and friction reduction in water salinity exceeding 200,000 TDS.\u0000 Two variations of the polymer architecture are described: a product that operates effectively in fluid below 100,000 TDS such as seawater (SW-HVFR) and a second high brine product that works in all fluids up to and above 200,000 TDS (HB-HVFR). Proppant transport was studied dynamically using a slot flow apparatus, which demonstrated performance with the new system that exceeded guar and greatly exceeded other conventional HVFRs at equivalent polymer loadings. Slot flow results indicated consistent transport performance throughout the salinity range investigated by proper selection of SW-HVFR and HB-HVFR.\u0000 Although high shear viscosity of the new polymers was inferior to that of guar, advanced rheological studiesindicated that the superior performance was due to enhanced viscosity and unusually high elasticity of the derived fluids within the relevant shear rate range between 1 and 100 s−1. In addition, anomalous dependence of viscosity on temperature is described, featuring a viscosity maximum above ambient temperature. This unusual rheology behavior was attributed to the associative polymer architecture of the new system.\u0000 The new HVFRs exhibit effective friction reduction within their intended salinity ranges as well as good tolerance toward biocides and clay control agents. Furthermore, the operational salinity range of SW-HVFR can be extended up to 200,000 TDS in the presence of certain production enhancement aids due to a synergistic effect on the polymer dissolution rate. Lastly, bottle testing indicated that the polymers are effectively broken by common oxidative breakers, enabling their flowback. These results demonstrate the flexibility of the new HVFR system to make total fluids utilizing any water source, enabling sustainable fracturing in a variety of situations.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84086602","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}
Alejandro De la Cruz Sasso, Hussien Alzaki, Rodny Masoud Zuleta, Nawaf Saud AlShammari
The efficient placement of cement as a verified barrier above a distinct permeable zone in any oil and gas well is a constant challenge faced by the industry. Absence of isolation behind the casing represents a deficiency to the required well integrity barriers. A compromised well barrier may result in casing corrosion, leaks, and eventually sustained casing pressure which might lead the loss of the asset and/or endanger the safety or workers and/or the environment. Ultimately, a compromised barrier implies compromised well integrity. Fluid displacement in ERD wells is different from conventional wells and the job conditions pose additional challenges. To ensure adequate cement placement in this ultra-ERD well, several challenges had to be addressed. Hence, an optimized cement placement method that focused on ECD management to prevent induced loss circulation included maintaining fluids displacement regimes, fluid density, and hydraulic friction hierarchy. Moreover, casing centralization was imperative. Limiting casing string movement once the string deployed successfully to bottom equally added to the challenge. A system's approach was utilized to achieve the level of optimization desired. Slurry rheology and fluid loss control were adjusted. A pumping schedulethat ensured that optimum displacement efficiencies were achieved in line with the designed rheology was used. The impact of pump rates on downhole ECD regimes were equally evaluated and confirmed to be fit-for-purpose. Mud conditioning prior to the cement displacement and spacer wettability were also of paramount importance. A centralizer spacing resulting in >70% stand-off was utilized. These optimized practices represented the results of 3D modeling used to understand the fluid dynamics, and its distribution under the influence of a horizontal static pipe. This work also presented a comprehensive sensitivity analysis not only on the effects of thermal thinning on fluid rheology, but also on gravitational forces acting on the fluids in an ERD well. After execution, a combination of cement bond logs, ultrasonic measurements, and advanced interpretation techniques were used to evaluate the cement bond quality. The logs showed an improved cement bonding with minimal to no channeling, and excellent radial cement coverage. As global hydrocarbon resources become harder to reach, ERD wells maybe required to access such subsurface targets. Adequate cementing well integrity is crucial to assuring the long-term integrity of such wells for the economic life of the assets. The practices implemented in this case history will contribute to expanding the tools and techniques available to engineers to achieving excellent barrier isolation in such wells.
{"title":"Longest 9 5/8? Casing Cementing in ERD Well, A Worldwide-Record","authors":"Alejandro De la Cruz Sasso, Hussien Alzaki, Rodny Masoud Zuleta, Nawaf Saud AlShammari","doi":"10.2523/iptc-22082-ms","DOIUrl":"https://doi.org/10.2523/iptc-22082-ms","url":null,"abstract":"\u0000 The efficient placement of cement as a verified barrier above a distinct permeable zone in any oil and gas well is a constant challenge faced by the industry. Absence of isolation behind the casing represents a deficiency to the required well integrity barriers. A compromised well barrier may result in casing corrosion, leaks, and eventually sustained casing pressure which might lead the loss of the asset and/or endanger the safety or workers and/or the environment.\u0000 Ultimately, a compromised barrier implies compromised well integrity. Fluid displacement in ERD wells is different from conventional wells and the job conditions pose additional challenges. To ensure adequate cement placement in this ultra-ERD well, several challenges had to be addressed. Hence, an optimized cement placement method that focused on ECD management to prevent induced loss circulation included maintaining fluids displacement regimes, fluid density, and hydraulic friction hierarchy. Moreover, casing centralization was imperative. Limiting casing string movement once the string deployed successfully to bottom equally added to the challenge.\u0000 A system's approach was utilized to achieve the level of optimization desired. Slurry rheology and fluid loss control were adjusted. A pumping schedulethat ensured that optimum displacement efficiencies were achieved in line with the designed rheology was used. The impact of pump rates on downhole ECD regimes were equally evaluated and confirmed to be fit-for-purpose. Mud conditioning prior to the cement displacement and spacer wettability were also of paramount importance. A centralizer spacing resulting in >70% stand-off was utilized. These optimized practices represented the results of 3D modeling used to understand the fluid dynamics, and its distribution under the influence of a horizontal static pipe. This work also presented a comprehensive sensitivity analysis not only on the effects of thermal thinning on fluid rheology, but also on gravitational forces acting on the fluids in an ERD well. After execution, a combination of cement bond logs, ultrasonic measurements, and advanced interpretation techniques were used to evaluate the cement bond quality. The logs showed an improved cement bonding with minimal to no channeling, and excellent radial cement coverage.\u0000 As global hydrocarbon resources become harder to reach, ERD wells maybe required to access such subsurface targets. Adequate cementing well integrity is crucial to assuring the long-term integrity of such wells for the economic life of the assets. The practices implemented in this case history will contribute to expanding the tools and techniques available to engineers to achieving excellent barrier isolation in such wells.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87913287","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. T. Law, Engku Ali Aminulhakim Engku M. Shukri, Sukhveender Singh Sukhdev Singh, Sarah, Suet Hoey Lim, A. Muhamad
The hunt for more data at lesser the cost is a never-ending quest in the world of seismic acquisition. The latest offered solution in that vein – multisource 3D marine acquisition Here, we present the first-ever penta source 3D Marine Acquisition to be adopted commercially in Malaysian waters, largest survey area of its kind to date ~ 6920 sqkm of data in total. This paper provides an overview of the execution and the outline of the challenges faced, and adaptations made to absolve it, in both acquiring and processing the penta source data. The results are compared with pre-existing legacy data, and with it, recommendations for better acquisition efficiency and processing results.
{"title":"Penta Source 3D Marine Acquisition in the Sarawak Basin, Malaysia","authors":"C. T. Law, Engku Ali Aminulhakim Engku M. Shukri, Sukhveender Singh Sukhdev Singh, Sarah, Suet Hoey Lim, A. Muhamad","doi":"10.2523/iptc-21887-ea","DOIUrl":"https://doi.org/10.2523/iptc-21887-ea","url":null,"abstract":"\u0000 \u0000 \u0000 The hunt for more data at lesser the cost is a never-ending quest in the world of seismic acquisition. The latest offered solution in that vein – multisource 3D marine acquisition Here, we present the first-ever penta source 3D Marine Acquisition to be adopted commercially in Malaysian waters, largest survey area of its kind to date ~ 6920 sqkm of data in total. This paper provides an overview of the execution and the outline of the challenges faced, and adaptations made to absolve it, in both acquiring and processing the penta source data. The results are compared with pre-existing legacy data, and with it, recommendations for better acquisition efficiency and processing results.\u0000","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88286712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Pooniwala, I. Brohi, A. Waheed, AbdulMuqtadir Khan, Zahaezuani Rafiq Hamidon
Post-fracturing cleanup and production revival in sub-hydrostatic wells can be challenging. The complexity is amplified in sub-hydrostatic multistage horizontal wells because, by the time the fracturing treatment is concluded, the gas phase of the energized fracturing fluids used during the initial stages of the fracturing treatment dissipates. In the subject sub-hydrostatic well, coiled tubing (CT) with a real-time telemetry system was utilized over a standard nitrogen lifting intervention utilizing conventional CT to revive a hydraulically fractured well due to its capabilities to enable real-time decisions using live bottom-hole data. Acid fracturing using an energized fluid treatment was conducted in the subject gas well completed with a multistage open-hole completion system using isolation packers and sleeves. As the subject well was sub-hydrostatic, it was decided to utilize the CT with real-time telemetry system to gain value from its associated downhole parameters during the cleanup phase to alleviate the chances of successfully lifting the well. The well was placed in an area with prolific offset producers; hence, there were high production expectations from this well. A review of the well indicated a decreasing trend of reservoir pressure from heel to toe of the lateral, possibly contributing to lower stresses and potential crossflow between stages. Hence, the diverter concentrations and volumes per stage and nitrogen rates were maximized for each new fracturing stage to attempt to create new fractures. Considering the challenges with the well, it was essential that the N2 lifting operation parameters should be optimized to enhance drawdown. It was decided to utilize CT with a real-time telemetry system to control drawdown parameters better and maximize the possibility of success. Real-time downhole pressure measurements were utilized to accurately identify the fluid gradient followed by real-time evaluation and monitoring of the well behavior during N2 lifting operations. The real-time downhole data collected enabled on-the-fly intervention optimization leading to transforming the well into an economic producer. The integrated post-treatment analysis workflow provided a robust insight into fracture treatment design and evaluation, reservoir imbibition perspective, openhole completion practices, and the importance of real-time telemetry for challenging interventions. The lessons learned that are presented in this paper could act as a guide to contribute to operational efficiency enhancements and cost savings in other projects.
{"title":"Adding a New Lease of Life to a Sub-Hydrostatic Hydraulically Fractured Gas Well Using Coiled Tubing with Real-Time Telemetry","authors":"S. Pooniwala, I. Brohi, A. Waheed, AbdulMuqtadir Khan, Zahaezuani Rafiq Hamidon","doi":"10.2523/iptc-22374-ms","DOIUrl":"https://doi.org/10.2523/iptc-22374-ms","url":null,"abstract":"\u0000 Post-fracturing cleanup and production revival in sub-hydrostatic wells can be challenging. The complexity is amplified in sub-hydrostatic multistage horizontal wells because, by the time the fracturing treatment is concluded, the gas phase of the energized fracturing fluids used during the initial stages of the fracturing treatment dissipates. In the subject sub-hydrostatic well, coiled tubing (CT) with a real-time telemetry system was utilized over a standard nitrogen lifting intervention utilizing conventional CT to revive a hydraulically fractured well due to its capabilities to enable real-time decisions using live bottom-hole data.\u0000 Acid fracturing using an energized fluid treatment was conducted in the subject gas well completed with a multistage open-hole completion system using isolation packers and sleeves. As the subject well was sub-hydrostatic, it was decided to utilize the CT with real-time telemetry system to gain value from its associated downhole parameters during the cleanup phase to alleviate the chances of successfully lifting the well.\u0000 The well was placed in an area with prolific offset producers; hence, there were high production expectations from this well. A review of the well indicated a decreasing trend of reservoir pressure from heel to toe of the lateral, possibly contributing to lower stresses and potential crossflow between stages. Hence, the diverter concentrations and volumes per stage and nitrogen rates were maximized for each new fracturing stage to attempt to create new fractures. Considering the challenges with the well, it was essential that the N2 lifting operation parameters should be optimized to enhance drawdown. It was decided to utilize CT with a real-time telemetry system to control drawdown parameters better and maximize the possibility of success. Real-time downhole pressure measurements were utilized to accurately identify the fluid gradient followed by real-time evaluation and monitoring of the well behavior during N2 lifting operations. The real-time downhole data collected enabled on-the-fly intervention optimization leading to transforming the well into an economic producer.\u0000 The integrated post-treatment analysis workflow provided a robust insight into fracture treatment design and evaluation, reservoir imbibition perspective, openhole completion practices, and the importance of real-time telemetry for challenging interventions. The lessons learned that are presented in this paper could act as a guide to contribute to operational efficiency enhancements and cost savings in other projects.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87118382","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}
Permeability is a fundamentally important property of reservoir rocks that governs the flow of the reservoir fluids and production rates. Many methods have been developed to estimate permeability, including well established and documented laboratory measurements on whole core and plugs, analysis of formation test data, and analysis of production. Obtaining permeability from nuclear magnetic resonance (NMR) T1 or T2 has proven to be a cost effective method that can provide continuous permeability along a wellbore. This method uses a transform function on NMR well log data to calculate permeability. An accurate NMR permeability transform requires calibration to fit local data of a specific field based on measured data from representative cores from the field. The general form of the permeability transform developed for conventional sandstone and carbonate reservoirs does not work well for extremely tight reservoirs such as source rocks. Here we show a generic optimization method to find the optimal permeability transform for any tight reservoirs using NMR log data and laboratory measured permeability data from samples at selected depth of the logged well. This optimization method is applied to a source rock well and a permeability transform was obtained. The transform is a function of the movable fluid in the rock and logarithm mean of the NMR relaxation time. The permeability calculated from the transform is comparable to measured permeability from core samples.
{"title":"Optimization of NMR Permeability Transform and Application to a Source Rock Reservoir","authors":"Jin-Hong Chen, Stacey M Althaus, M. Boudjatit","doi":"10.2523/iptc-22195-ea","DOIUrl":"https://doi.org/10.2523/iptc-22195-ea","url":null,"abstract":"\u0000 Permeability is a fundamentally important property of reservoir rocks that governs the flow of the reservoir fluids and production rates. Many methods have been developed to estimate permeability, including well established and documented laboratory measurements on whole core and plugs, analysis of formation test data, and analysis of production. Obtaining permeability from nuclear magnetic resonance (NMR) T1 or T2 has proven to be a cost effective method that can provide continuous permeability along a wellbore. This method uses a transform function on NMR well log data to calculate permeability. An accurate NMR permeability transform requires calibration to fit local data of a specific field based on measured data from representative cores from the field. The general form of the permeability transform developed for conventional sandstone and carbonate reservoirs does not work well for extremely tight reservoirs such as source rocks. Here we show a generic optimization method to find the optimal permeability transform for any tight reservoirs using NMR log data and laboratory measured permeability data from samples at selected depth of the logged well. This optimization method is applied to a source rock well and a permeability transform was obtained. The transform is a function of the movable fluid in the rock and logarithm mean of the NMR relaxation time. The permeability calculated from the transform is comparable to measured permeability from core samples.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85359310","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}
Idris Al Siyabi, Aiman Al Shukaili, M. Al Ajmi, R. Mujaini, Moosa Al Amri, A. Al Ghufaili, Marwa Al Harrasi, Bader Al Ma'Mari
Steam generation for the Enhanced Oil Recovery (EOR) using steam injection is considered as one of critical production process in heavy oil industry. It requires a massive amount of thermal energy to generate one ton of steam. The conventional steam generation processes use natural gas as fuel where high amount of emissions is released to ambient. The recent cost reductions of the energy generated by the renewable energy attracts organizations to adopt it. Different technologies such as PV, CSP, electric heaters and thermal energy storage can be integrated to generate steam using the renewable resources of solar and wind. The study explores the different potential of adopting such technologies and the challenges associated with them where the mismatch between the supply and the demand of energy is the main challenge and could be mitigated through storage solutions and the consideration of hybrid system with the conventional steam generation.
{"title":"Opportunities and Challenges of Steam Generation Using Renewable Energy for Enhanced Oil Recovery Applications: Concepts Overview","authors":"Idris Al Siyabi, Aiman Al Shukaili, M. Al Ajmi, R. Mujaini, Moosa Al Amri, A. Al Ghufaili, Marwa Al Harrasi, Bader Al Ma'Mari","doi":"10.2523/iptc-22370-ms","DOIUrl":"https://doi.org/10.2523/iptc-22370-ms","url":null,"abstract":"\u0000 Steam generation for the Enhanced Oil Recovery (EOR) using steam injection is considered as one of critical production process in heavy oil industry. It requires a massive amount of thermal energy to generate one ton of steam. The conventional steam generation processes use natural gas as fuel where high amount of emissions is released to ambient. The recent cost reductions of the energy generated by the renewable energy attracts organizations to adopt it. Different technologies such as PV, CSP, electric heaters and thermal energy storage can be integrated to generate steam using the renewable resources of solar and wind. The study explores the different potential of adopting such technologies and the challenges associated with them where the mismatch between the supply and the demand of energy is the main challenge and could be mitigated through storage solutions and the consideration of hybrid system with the conventional steam generation.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85732064","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}
L. Dumitrache, Alistair Roy, Anastasia Bird, B. Goktas, C. Sorgi, Reginald Stanley, V. De Gennaro, E. Eswein, J. Abbott
The integration of data and discipline specific knowledge is a common challenge when attempting to optimize or accelerate an asset's recovery through hydraulic fracture stimulations. Any potential omission of data or understanding will increase uncertainty and a project's chance of failure. Therefore, when looking to optimize the production of a given asset, it is key to take a holistic approach that breaks down any technical and organisational barriers. This project couples the output of the different subsurface and stimulation disciplines to reduce the uncertainty associated with the production forecast of planned stimulation designs. The following paper presents the integrated approach for the Graben sector of UK's North Sea Clair oil field, largest oil field currently in Europe. Geophysicists, petrophysicists, and geologists generate a static model which is calibrated and validated by reservoir engineers through dynamic reservoir simulation. This model is used to identify the optimum exploitation scenario for a hydrocarbon reservoir and is assessed by the geomechanics engineer to deduce the subsurface stresses and strains to create a 3D mechanical earth model. The multidisciplinary validated representation is handed over to the stimulation engineer to implement various treatments, either performed or to be performed. Once these treatments are designed, the reservoir engineer produces a production forecast which is then fed back to all team members involved in the process, enabling an optimization loop. Considering that this is a multi-well (producers and injector) study, any inference is reflected by the analysis and the optimum hydraulic fracture design is chosen for implementation by an offshore stimulation vessel. Traditionally, for forecasting purposes, hydraulic fractures can be implemented using conventional reservoir simulation; however, these are very much approximated models of what the stimulation engineers are designing and implementing. Often, the reservoir, production, stimulation engineers can come up with individual forecasts that are obtained independently and omit basic information. A typical example is the way stresses might change due to stimulation and production and the possibility to account for them in an integrated way. The proposed workflow eliminates these shortcomings, and the asset team delivers a single forecast of the exact fracture design considering a fully consistent model of the subsurface, which is to be implemented by the stimulation vessel for the different wells.
{"title":"A Multidisciplinary Approach to Production Optimization through Hydraulic Fracturing Stimulation and Geomechanical Modelling in Clair Field","authors":"L. Dumitrache, Alistair Roy, Anastasia Bird, B. Goktas, C. Sorgi, Reginald Stanley, V. De Gennaro, E. Eswein, J. Abbott","doi":"10.2523/iptc-22293-ms","DOIUrl":"https://doi.org/10.2523/iptc-22293-ms","url":null,"abstract":"\u0000 The integration of data and discipline specific knowledge is a common challenge when attempting to optimize or accelerate an asset's recovery through hydraulic fracture stimulations. Any potential omission of data or understanding will increase uncertainty and a project's chance of failure. Therefore, when looking to optimize the production of a given asset, it is key to take a holistic approach that breaks down any technical and organisational barriers.\u0000 This project couples the output of the different subsurface and stimulation disciplines to reduce the uncertainty associated with the production forecast of planned stimulation designs. The following paper presents the integrated approach for the Graben sector of UK's North Sea Clair oil field, largest oil field currently in Europe.\u0000 Geophysicists, petrophysicists, and geologists generate a static model which is calibrated and validated by reservoir engineers through dynamic reservoir simulation. This model is used to identify the optimum exploitation scenario for a hydrocarbon reservoir and is assessed by the geomechanics engineer to deduce the subsurface stresses and strains to create a 3D mechanical earth model. The multidisciplinary validated representation is handed over to the stimulation engineer to implement various treatments, either performed or to be performed. Once these treatments are designed, the reservoir engineer produces a production forecast which is then fed back to all team members involved in the process, enabling an optimization loop. Considering that this is a multi-well (producers and injector) study, any inference is reflected by the analysis and the optimum hydraulic fracture design is chosen for implementation by an offshore stimulation vessel.\u0000 Traditionally, for forecasting purposes, hydraulic fractures can be implemented using conventional reservoir simulation; however, these are very much approximated models of what the stimulation engineers are designing and implementing. Often, the reservoir, production, stimulation engineers can come up with individual forecasts that are obtained independently and omit basic information. A typical example is the way stresses might change due to stimulation and production and the possibility to account for them in an integrated way. The proposed workflow eliminates these shortcomings, and the asset team delivers a single forecast of the exact fracture design considering a fully consistent model of the subsurface, which is to be implemented by the stimulation vessel for the different wells.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"02 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85910316","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}
Salman. F. Nofal, Fazeel Ahmad, Dr. Ahmad Shmakhy, Zohaib Ghous Channa, Arlen Sarsekov, Yassar Goraya Inayat, Sundos Ibrahim Alabed, Manal I. Albeshr, Ahmed Mohammed Al Seiari, W. Chehabi, T. Jørgensen, Kirstian Solhaug, R. Gill, Viljoen Duvenhage
Demonstrate technology effectiveness following improvements to system to increase robustness and refine operations following the initial Pilot Well deployed January 2016. The first Abu Dhabi Offshore well was deemed to be a success (reference SPE-183465-MS) despite deployment challenges during the lower completion phase. There was an opportunity to address these challenges for the second well, in the deployment of well -1 which from an operational perspective was textbook. In the search for an improvement to the Productivity index (PI), multi-lateral acid jetting technology was adopted as a more effective approach to typical drainage methods. With conventional stimulation techniques being limited in effectiveness and often leaving significant volumes of recoverable reserves out of reach, an alternative approach was required to create new connections within the reservoir. This technology effectively creates connections to layers previously separated by very tight, low permeability barriers to dramatically increase recovery factors across carbonate reservoirs. In a single multi-rate pumping sequence, needles were extended to create channels into the reservoir layers, using acid jetting technology to achieve vertical connectivity and improve production rates. Currently, up to 60 subs can be deployed in a signle well bore. With each sub capable of deploying 4 needles at 90 degrees perpendicular to the wellbore and up to 40 feet in length, multiple micro-laterals are created throughout the reservoir. During this case study, 10 sub-assemblies of the multi-lateral acid jetting technology system were installed, creating 40 micro-laterals, which significantly improved access to reserves. These laterals remain in the well, essentially leaving a permanently installed lower liner with full bore access to TD. Following successful adoption of this technology, the well has been producing for a year with positive results. Multi Rate test/PLT/Memory Gauge data all confirms a productivity index increase of 120%. This paper describes the process of candidate selection, completion design, operational challenges, deployment, post job analysis, system improvement and lessons learnt. Multilateral acid jetting technology has evolved and improved over recent years and the primary differentiators highlighted in this paper are as follows: The continuous enhancement of multi-lateral acid jetting technology is playing a key role in driving increased efficiency in field development planning. By reducing the total well requirement for the reservoir, whilst simultaneously increasing recoverable reserves, the technology is at the forefront of facilitating the future state of field development.
{"title":"Multi-Lateral Jetting Technology Results in a 150% Uplift in Production During a Second Offshore Application in Abu Dhabi Offshore Field","authors":"Salman. F. Nofal, Fazeel Ahmad, Dr. Ahmad Shmakhy, Zohaib Ghous Channa, Arlen Sarsekov, Yassar Goraya Inayat, Sundos Ibrahim Alabed, Manal I. Albeshr, Ahmed Mohammed Al Seiari, W. Chehabi, T. Jørgensen, Kirstian Solhaug, R. Gill, Viljoen Duvenhage","doi":"10.2523/iptc-21959-ea","DOIUrl":"https://doi.org/10.2523/iptc-21959-ea","url":null,"abstract":"\u0000 \u0000 \u0000 Demonstrate technology effectiveness following improvements to system to increase robustness and refine operations following the initial Pilot Well deployed January 2016.\u0000 The first Abu Dhabi Offshore well was deemed to be a success (reference SPE-183465-MS) despite deployment challenges during the lower completion phase. There was an opportunity to address these challenges for the second well, in the deployment of well -1 which from an operational perspective was textbook.\u0000 In the search for an improvement to the Productivity index (PI), multi-lateral acid jetting technology was adopted as a more effective approach to typical drainage methods. With conventional stimulation techniques being limited in effectiveness and often leaving significant volumes of recoverable reserves out of reach, an alternative approach was required to create new connections within the reservoir. This technology effectively creates connections to layers previously separated by very tight, low permeability barriers to dramatically increase recovery factors across carbonate reservoirs.\u0000 \u0000 \u0000 \u0000 In a single multi-rate pumping sequence, needles were extended to create channels into the reservoir layers, using acid jetting technology to achieve vertical connectivity and improve production rates.\u0000 Currently, up to 60 subs can be deployed in a signle well bore. With each sub capable of deploying 4 needles at 90 degrees perpendicular to the wellbore and up to 40 feet in length, multiple micro-laterals are created throughout the reservoir.\u0000 During this case study, 10 sub-assemblies of the multi-lateral acid jetting technology system were installed, creating 40 micro-laterals, which significantly improved access to reserves. These laterals remain in the well, essentially leaving a permanently installed lower liner with full bore access to TD.\u0000 \u0000 \u0000 \u0000 Following successful adoption of this technology, the well has been producing for a year with positive results. Multi Rate test/PLT/Memory Gauge data all confirms a productivity index increase of 120%.\u0000 This paper describes the process of candidate selection, completion design, operational challenges, deployment, post job analysis, system improvement and lessons learnt.\u0000 \u0000 \u0000 \u0000 Multilateral acid jetting technology has evolved and improved over recent years and the primary differentiators highlighted in this paper are as follows:\u0000 The continuous enhancement of multi-lateral acid jetting technology is playing a key role in driving increased efficiency in field development planning. By reducing the total well requirement for the reservoir, whilst simultaneously increasing recoverable reserves, the technology is at the forefront of facilitating the future state of field development.\u0000","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90223172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, many machine-learning models have been developed to predict future production of oil in gas in "shales". Long-short term memory (LSTM), the most widely used model, relies on the long-term production history for a reasonably accurate production forecast. All analytical and machine learning models, including LSTM, fail miserably in the absence of long production history. Our goal is to present a novel method of production forecasting using only 24 months of production data. The first and secondorder derivatives of the distance traveled give speed and acceleration to describe the trajectory and dynamics of a moving vehicle. Similarly, higher-order derivatives of hydrocarbon/water production rate vs. time uncover hidden patterns and fluctuations in a well that act as differential markers of its future recovery factor (RF). In this paper, we couple production data and their higher-order derivatives with other known parameters for a well, i.e., well length and initial production. The time-series data are passed into a Convolutional Neural Network (CNN) with two hidden layers of 16 nodes each, and one output layer. The model is trained to predict recovery factor (RF) in the 10th year of production. We analyze the first 24 months of production data for the Barnett (1500), Marcellus (800), Haynesville (800), and Eagle Ford (1000) shale wells. All wells have a minimum pressure interference time of 34 months. The production rate vs. time and its first, second, and third-order derivatives are coupled with the well length and initial production rate, and the data are normalized with their respective maxima. For the Barnett wells, the CNN model predicts recovery factors in their 10th year of production with an average accuracy of 90%. For the Marcellus, Haynesville, and Eagle Ford wells, the prediction accuracy in the 8th year of production is 89%, 92%, and 91%, respectively. Further, we divide the wells into three groups (A, B, C) depending on the range of their recovery factor (A:RF=0-0.3, B:RF=0.3-0.6, and C:RF=0.6-0.9). We show that the clusters of wells grouped by their RFs strongly correlate with the distribution of the higher-order de rivatives of production from these wells. Thus, we posit that the detailed production history and its derivatives are the most important variables that define distributions of maximum recoverable hydrocarbon from a source rock. Our novel method uses only 24 months of production data to predict future recovery factor with an outstanding average accuracy of 90%. We show that the higher-order derivatives of high-resolution production data available from the operators could be an excellent tool for well screening and predicting future production with reasonable accuracy.
{"title":"Higher-Order Derivatives of Production Rate and Convolutional Neural Network for Production Forecasts","authors":"Syed Tabish Haider, T. Patzek","doi":"10.2523/iptc-22486-ms","DOIUrl":"https://doi.org/10.2523/iptc-22486-ms","url":null,"abstract":"\u0000 In recent years, many machine-learning models have been developed to predict future production of oil in gas in \"shales\". Long-short term memory (LSTM), the most widely used model, relies on the long-term production history for a reasonably accurate production forecast. All analytical and machine learning models, including LSTM, fail miserably in the absence of long production history. Our goal is to present a novel method of production forecasting using only 24 months of production data. The first and secondorder derivatives of the distance traveled give speed and acceleration to describe the trajectory and dynamics of a moving vehicle. Similarly, higher-order derivatives of hydrocarbon/water production rate vs. time uncover hidden patterns and fluctuations in a well that act as differential markers of its future recovery factor (RF). In this paper, we couple production data and their higher-order derivatives with other known parameters for a well, i.e., well length and initial production. The time-series data are passed into a Convolutional Neural Network (CNN) with two hidden layers of 16 nodes each, and one output layer. The model is trained to predict recovery factor (RF) in the 10th year of production. We analyze the first 24 months of production data for the Barnett (1500), Marcellus (800), Haynesville (800), and Eagle Ford (1000) shale wells. All wells have a minimum pressure interference time of 34 months. The production rate vs. time and its first, second, and third-order derivatives are coupled with the well length and initial production rate, and the data are normalized with their respective maxima. For the Barnett wells, the CNN model predicts recovery factors in their 10th year of production with an average accuracy of 90%. For the Marcellus, Haynesville, and Eagle Ford wells, the prediction accuracy in the 8th year of production is 89%, 92%, and 91%, respectively. Further, we divide the wells into three groups (A, B, C) depending on the range of their recovery factor (A:RF=0-0.3, B:RF=0.3-0.6, and C:RF=0.6-0.9). We show that the clusters of wells grouped by their RFs strongly correlate with the distribution of the higher-order de rivatives of production from these wells. Thus, we posit that the detailed production history and its derivatives are the most important variables that define distributions of maximum recoverable hydrocarbon from a source rock. Our novel method uses only 24 months of production data to predict future recovery factor with an outstanding average accuracy of 90%. We show that the higher-order derivatives of high-resolution production data available from the operators could be an excellent tool for well screening and predicting future production with reasonable accuracy.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80842211","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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