Polymer transport and retention affect oil recovery and economic feasibility of EOR processes. Most studies on polymer transport have focused on sandstones with permeabilities (k) higher than 200 mD. A limited number of studies were conducted in carbonates with k less than 100 mD and very few in the presence of residual oil. In this work, transport of four polymers with different molecular weights (MW) and functional groups are studied in Edwards Yellow outcrop cores (k<50 mD) with and without residual oil saturation (Sor). The retention of polymers was estimated by both the material balance method and the double-bank method. The polymer concentration was measured by both the total organic carbon (TOC) analyzer and the capillary tube rheology. Partially hydrolyzed acrylamide (HPAM) polymers exhibited high retention (> 150 μg/g), inaccessible pore volume (IPV) greater than 7%, and high residual resistance factor (>9). A sulfonated polyacrylamide (AN132), showed low retentions (< 20 μg/g) and low IPV. The residual resistance factor (RRF) of AN132 in the water-saturated rock was less than 2, indicating little blocking of pore throats in these tight rocks. The retention and RRF of the AN132 polymer increased in the presence of residual oil saturation due to partial blocking of the smaller pore throats available for polymer propagation in an oil-wet core.
{"title":"Transport of Polymers in Low Permeability Carbonate Rocks","authors":"Haofeng Song, P. Ghosh, K. Mohanty","doi":"10.2118/206024-ms","DOIUrl":"https://doi.org/10.2118/206024-ms","url":null,"abstract":"\u0000 Polymer transport and retention affect oil recovery and economic feasibility of EOR processes. Most studies on polymer transport have focused on sandstones with permeabilities (k) higher than 200 mD. A limited number of studies were conducted in carbonates with k less than 100 mD and very few in the presence of residual oil. In this work, transport of four polymers with different molecular weights (MW) and functional groups are studied in Edwards Yellow outcrop cores (k<50 mD) with and without residual oil saturation (Sor). The retention of polymers was estimated by both the material balance method and the double-bank method. The polymer concentration was measured by both the total organic carbon (TOC) analyzer and the capillary tube rheology. Partially hydrolyzed acrylamide (HPAM) polymers exhibited high retention (> 150 μg/g), inaccessible pore volume (IPV) greater than 7%, and high residual resistance factor (>9). A sulfonated polyacrylamide (AN132), showed low retentions (< 20 μg/g) and low IPV. The residual resistance factor (RRF) of AN132 in the water-saturated rock was less than 2, indicating little blocking of pore throats in these tight rocks. The retention and RRF of the AN132 polymer increased in the presence of residual oil saturation due to partial blocking of the smaller pore throats available for polymer propagation in an oil-wet core.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78934018","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}
Do we analyze on why can even the most reliable turbomachinery are getting failure and stopped? In some cases, it's all about bad installation or design literally. This paper explores the challenges one site had with repeated failure of lube oil fin fan coolers coupling which caused the unit availability of more than 3 months. It outlines the troubleshooting attempts made to remedy this issue, its root cause, and the resulting solution. This issue occurred at a site with a train configuration of motor driven centrifugal compressors. The plant lube oil system has been configured with 3 trains. Each train has been configured with Main electric motor + Vorecon Gearbox + Low Pressure centrifugal compressor + High Pressure centrifugal compressor. Lube oil system of the train has been configured as 2 lube oil coolers and 2 working oil coolers. Lube oil coolers are having fins with air cooler type. Air is supplied by fin fans and each train has 2 lube oil cooler fans and 2 working oil cooler fans. In total site has 3 trains x 4 fin fans so it has 12 fin fan cooler fans. All cooler fans are driven by electric motor which is coupled with gearbox and gear box is connected with cooler fan. During normal operation of working oil cooler fan A- stopped rotation suddenly from normal operation. During investigation, motor shaft was found running freely. No movement was seen on cooler fan. Coupling between motor to gearbox was inspected. Coupling is shear plate coupling. Its spacer flexible element were found broken into several pieces. Further investigation revealed that motor coupling hub was moving free axially back and forth due to clearance between motor shaft to coupling hub internal diameter. Motor side Coupling hub bolt hole was found with loss of material and ovality in shape. Hub locking Allen screw was found in damaged condition. Missing materials were noted and broken shear plate materials were found around coupling guard area. While site team was conducting the investigation on the unit A, similar incident occurred in next unit and other 3 units with 2 days difference between them. During detailed investigation it has been noted that all motor to gear box coupling are shear plates and shear plates were broken. Coupling hub was found loose and coupling hub locking screw was found broken or partial damage.
{"title":"Case Study: Consecutive Failure of Lube Oil Cooler Fans Coupling","authors":"A. Manikandan, Zeeshan Anwar","doi":"10.2118/206120-ms","DOIUrl":"https://doi.org/10.2118/206120-ms","url":null,"abstract":"\u0000 Do we analyze on why can even the most reliable turbomachinery are getting failure and stopped? In some cases, it's all about bad installation or design literally. This paper explores the challenges one site had with repeated failure of lube oil fin fan coolers coupling which caused the unit availability of more than 3 months. It outlines the troubleshooting attempts made to remedy this issue, its root cause, and the resulting solution.\u0000 This issue occurred at a site with a train configuration of motor driven centrifugal compressors. The plant lube oil system has been configured with 3 trains. Each train has been configured with Main electric motor + Vorecon Gearbox + Low Pressure centrifugal compressor + High Pressure centrifugal compressor. Lube oil system of the train has been configured as 2 lube oil coolers and 2 working oil coolers. Lube oil coolers are having fins with air cooler type. Air is supplied by fin fans and each train has 2 lube oil cooler fans and 2 working oil cooler fans. In total site has 3 trains x 4 fin fans so it has 12 fin fan cooler fans. All cooler fans are driven by electric motor which is coupled with gearbox and gear box is connected with cooler fan.\u0000 During normal operation of working oil cooler fan A- stopped rotation suddenly from normal operation. During investigation, motor shaft was found running freely. No movement was seen on cooler fan. Coupling between motor to gearbox was inspected. Coupling is shear plate coupling. Its spacer flexible element were found broken into several pieces. Further investigation revealed that motor coupling hub was moving free axially back and forth due to clearance between motor shaft to coupling hub internal diameter. Motor side Coupling hub bolt hole was found with loss of material and ovality in shape. Hub locking Allen screw was found in damaged condition. Missing materials were noted and broken shear plate materials were found around coupling guard area.\u0000 While site team was conducting the investigation on the unit A, similar incident occurred in next unit and other 3 units with 2 days difference between them.\u0000 During detailed investigation it has been noted that all motor to gear box coupling are shear plates and shear plates were broken. Coupling hub was found loose and coupling hub locking screw was found broken or partial damage.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72675385","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 previous study by the authors on synthetic fractal-fracture networks showed that lacunarity, a parameter that quantifies scale-dependent clustering in patterns, can be used as a proxy for connectivity and also, is an indicator of fluid flow in such model networks. In this research, we apply the concepts thus developed to the study of fractured reservoir analogs and seek solutions to more practical problems faced by modelers in the oil and gas industry. A set of seven nested fracture networks from the Devonian Sandstone of Hornelen Basin, Norway that have the same fractal-dimension but are mapped at different scales and resolutions is considered. We compare these seven natural fracture maps in terms of their lacunarity and connectivity values to test whether the former is a reasonable indicator of the latter. Additionally, these maps are also flow simulated by implementing a fracture continuum model and using a streamline simulator, TRACE3D. The values of lacunarity, connectivity and fluid recovery thus obtained are pairwise correlated with one another to look for possible relationships. The results indicate that while fracture maps that have the same fractal dimension show almost similar connectivity values, there exist subtle differences such that both the connectivity and clustering values change systematically with the scale at which the fracture networks are mapped. It is further noted that there appears to be a very good correlation between clustering, connectivity, and fluid recovery values for these fracture networks that belong to the same fractal system. The overall results indicate that while the fractal dimension is an important parameter for characterizing a specific type of fracture network geometry, it is the lacunarity or scale-dependent clustering attribute that controls connectivity in fracture maps and hence the flow properties. This research may prove helpful in quickly evaluating connectivity of fracture networks based on the lacunarity parameter. This parameter can therefore, be used for calibrating Discrete Fracture Network (DFN) models with respect to connectivity of reservoir analogs and can possibly replace the fractal dimension which is more commonly used in software that model DFNs. Additionally, while lacunarity has been mostly used for understanding network geometry in terms of clustering, we, for the first time, show how this may be directly used for understanding the potential flow behavior of fracture networks.
{"title":"Clustering, Connectivity and Flow in Naturally Fractured Reservoir Analogs","authors":"A. Sahu, A. Roy","doi":"10.2118/206009-ms","DOIUrl":"https://doi.org/10.2118/206009-ms","url":null,"abstract":"\u0000 A previous study by the authors on synthetic fractal-fracture networks showed that lacunarity, a parameter that quantifies scale-dependent clustering in patterns, can be used as a proxy for connectivity and also, is an indicator of fluid flow in such model networks. In this research, we apply the concepts thus developed to the study of fractured reservoir analogs and seek solutions to more practical problems faced by modelers in the oil and gas industry. A set of seven nested fracture networks from the Devonian Sandstone of Hornelen Basin, Norway that have the same fractal-dimension but are mapped at different scales and resolutions is considered. We compare these seven natural fracture maps in terms of their lacunarity and connectivity values to test whether the former is a reasonable indicator of the latter. Additionally, these maps are also flow simulated by implementing a fracture continuum model and using a streamline simulator, TRACE3D. The values of lacunarity, connectivity and fluid recovery thus obtained are pairwise correlated with one another to look for possible relationships. The results indicate that while fracture maps that have the same fractal dimension show almost similar connectivity values, there exist subtle differences such that both the connectivity and clustering values change systematically with the scale at which the fracture networks are mapped. It is further noted that there appears to be a very good correlation between clustering, connectivity, and fluid recovery values for these fracture networks that belong to the same fractal system. The overall results indicate that while the fractal dimension is an important parameter for characterizing a specific type of fracture network geometry, it is the lacunarity or scale-dependent clustering attribute that controls connectivity in fracture maps and hence the flow properties. This research may prove helpful in quickly evaluating connectivity of fracture networks based on the lacunarity parameter. This parameter can therefore, be used for calibrating Discrete Fracture Network (DFN) models with respect to connectivity of reservoir analogs and can possibly replace the fractal dimension which is more commonly used in software that model DFNs. Additionally, while lacunarity has been mostly used for understanding network geometry in terms of clustering, we, for the first time, show how this may be directly used for understanding the potential flow behavior of fracture networks.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87209489","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 mechanical properties of hydrocarbon reservoirs significantly depend on the elastic properties of the fluids occupying the pore space in the rock frame. Accurate data and models for the mechanical properties of fluid mixtures in a petroleum reservoir containing supercritical CO2 should be available at the same reservoir conditions for reliable design of well-completion, maximizing reservoir productivity, and minimizing risk in drilling operations. This work investigates the change in the bulk modulus of the higher hydrocarbon fluid (decane C10H22) after the injection with supercritical CO2 at reservoir conditions. The isothermal bulk modulus βT of liquids under pressure, simply defined as the first-order derivative of pressure with respect to volume, is determined in this study from the derivative of pressure with respect to density. The density data were obtained from experimental measurements of mixtures of supercritical CO2 + C10H22 for a range of CO2 mole fractions from 0 to 0.73, at temperatures from 40 to 137 °C and pressures up to 12000 psi. The isothermal derivative coefficients of the pressure as a function of density are reported for each CO2 concentration measured in this work. Common fluid-substitution models, including the Gassmann model, which is only valid for the isothermal regime, have limited predictive power because most fluids are treated as simple fluids, with their mechanical properties only characterized by their densities. However, under different environments, such as when supercritical CO2 is injected into the geological formation, the fluid phase and its mechanical properties can vary dramatically. At high pressure, the density of CO2 can equal to that of the hydrocarbon phase ρ(CO2)/ρ(C10H22) ≈ 1, while the bulk modulus of CO2 remains as low as only βT(CO2)/βT(C10H22) ≈ 7 %. Excessive decrease in the bulk modulus can easily cause subsidence, although the pore pressure and the fluid mixture density remain unchanged, even at pressures up to 4000 psi.
{"title":"Bulk Modulus of Hydrocarbon Fluids After Injection with Supercritical CO2 at Reservoir Conditions","authors":"Mohamed E. Kandil","doi":"10.2118/206277-ms","DOIUrl":"https://doi.org/10.2118/206277-ms","url":null,"abstract":"\u0000 The mechanical properties of hydrocarbon reservoirs significantly depend on the elastic properties of the fluids occupying the pore space in the rock frame. Accurate data and models for the mechanical properties of fluid mixtures in a petroleum reservoir containing supercritical CO2 should be available at the same reservoir conditions for reliable design of well-completion, maximizing reservoir productivity, and minimizing risk in drilling operations. This work investigates the change in the bulk modulus of the higher hydrocarbon fluid (decane C10H22) after the injection with supercritical CO2 at reservoir conditions. The isothermal bulk modulus βT of liquids under pressure, simply defined as the first-order derivative of pressure with respect to volume, is determined in this study from the derivative of pressure with respect to density. The density data were obtained from experimental measurements of mixtures of supercritical CO2 + C10H22 for a range of CO2 mole fractions from 0 to 0.73, at temperatures from 40 to 137 °C and pressures up to 12000 psi. The isothermal derivative coefficients of the pressure as a function of density are reported for each CO2 concentration measured in this work. Common fluid-substitution models, including the Gassmann model, which is only valid for the isothermal regime, have limited predictive power because most fluids are treated as simple fluids, with their mechanical properties only characterized by their densities. However, under different environments, such as when supercritical CO2 is injected into the geological formation, the fluid phase and its mechanical properties can vary dramatically. At high pressure, the density of CO2 can equal to that of the hydrocarbon phase ρ(CO2)/ρ(C10H22) ≈ 1, while the bulk modulus of CO2 remains as low as only βT(CO2)/βT(C10H22) ≈ 7 %. Excessive decrease in the bulk modulus can easily cause subsidence, although the pore pressure and the fluid mixture density remain unchanged, even at pressures up to 4000 psi.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91074157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reservoir characterization requires accurate prediction of multiple petrophysical properties such as bulk density (or acoustic impedance), porosity, and permeability. However, it remains a big challenge in heterogeneous reservoirs due to significant diagenetic impacts including dissolution, dolomitization, cementation, and fracturing. Most well logs lack the resolution to obtain rock properties in detail in a heterogenous formation. Therefore, it is pertinent to integrate core images into the prediction workflow. This study presents a new approach to solve the problem of obtaining the high-resolution multiple petrophysical properties, by combining machine learning (ML) algorithms and computer vision (CV) techniques. The methodology can be used to automate the process of core data analysis with a minimum number of plugs, thus reducing human effort and cost and improving accuracy. The workflow consists of conditioning and extracting features from core images, correlating well logs and core analysis with those features to build ML models, and applying the models on new cores for petrophysical properties predictions. The core images are preprocessed and analyzed using color models and texture recognition, to extract image characteristics and core textures. The image features are then aggregated into a profile in depth, resampled and aligned with well logs and core analysis. The ML regression models, including classification and regression trees (CART) and deep neural network (DNN), are trained and validated from the filtered training samples of relevant features and target petrophysical properties. The models are then tested on a blind test dataset to evaluate the prediction performance, to predict target petrophysical properties of grain density, porosity and permeability. The profile of histograms of each target property are computed to analyze the data distribution. The feature vectors are extracted from CV analysis of core images and gamma ray logs. The importance of each feature is generated by CART model to individual target, which may be used to reduce model complexity of future model building. The model performances are evaluated and compared on each target. We achieved reasonably good correlation and accuracy on the models, for example, porosity R2=49.7% and RMSE=2.4 p.u., and logarithmic permeability R2=57.8% and RMSE=0.53. The field case demonstrates that inclusion of core image attributes can improve petrophysical regression in heterogenous reservoirs. It can be extended to a multi-well setting to generate vertical distribution of petrophysical properties which can be integrated into reservoir modeling and characterization. Machine leaning algorithms can help automate the workflow and be flexible to be adjusted to take various inputs for prediction.
{"title":"Machine Learning for Multiple Petrophysical Properties Regression Based on Core Images and Well Logs in a Heterogenous Reservoir","authors":"T. Lin, M. Mezghani, Chicheng Xu, Weichang Li","doi":"10.2118/206089-ms","DOIUrl":"https://doi.org/10.2118/206089-ms","url":null,"abstract":"\u0000 Reservoir characterization requires accurate prediction of multiple petrophysical properties such as bulk density (or acoustic impedance), porosity, and permeability. However, it remains a big challenge in heterogeneous reservoirs due to significant diagenetic impacts including dissolution, dolomitization, cementation, and fracturing. Most well logs lack the resolution to obtain rock properties in detail in a heterogenous formation. Therefore, it is pertinent to integrate core images into the prediction workflow.\u0000 This study presents a new approach to solve the problem of obtaining the high-resolution multiple petrophysical properties, by combining machine learning (ML) algorithms and computer vision (CV) techniques. The methodology can be used to automate the process of core data analysis with a minimum number of plugs, thus reducing human effort and cost and improving accuracy. The workflow consists of conditioning and extracting features from core images, correlating well logs and core analysis with those features to build ML models, and applying the models on new cores for petrophysical properties predictions.\u0000 The core images are preprocessed and analyzed using color models and texture recognition, to extract image characteristics and core textures. The image features are then aggregated into a profile in depth, resampled and aligned with well logs and core analysis. The ML regression models, including classification and regression trees (CART) and deep neural network (DNN), are trained and validated from the filtered training samples of relevant features and target petrophysical properties. The models are then tested on a blind test dataset to evaluate the prediction performance, to predict target petrophysical properties of grain density, porosity and permeability. The profile of histograms of each target property are computed to analyze the data distribution. The feature vectors are extracted from CV analysis of core images and gamma ray logs. The importance of each feature is generated by CART model to individual target, which may be used to reduce model complexity of future model building. The model performances are evaluated and compared on each target. We achieved reasonably good correlation and accuracy on the models, for example, porosity R2=49.7% and RMSE=2.4 p.u., and logarithmic permeability R2=57.8% and RMSE=0.53.\u0000 The field case demonstrates that inclusion of core image attributes can improve petrophysical regression in heterogenous reservoirs. It can be extended to a multi-well setting to generate vertical distribution of petrophysical properties which can be integrated into reservoir modeling and characterization. Machine leaning algorithms can help automate the workflow and be flexible to be adjusted to take various inputs for prediction.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"210 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74709582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Water-alternating-gas (WAG) injection, both miscible and immiscible, is a widely used enhanced oil recovery method with over 80 field cases. Despite its prevalence, the numerical modeling of the physical processes involved remains poorly understood, and existing models often lack predictability. Part of the complexity stems from the component exchange between gas and oil and the hysteretic relative permeability effects. Thus, improving the reliability of numerical models requires the calibration of the equation of state (EOS) against phase behavior data from swelling/extraction and slim-tube tests, and the calibration of the three-phase relative permeability model against WAG coreflood experiments. This paper presents the results and interpretation of a complete set of two-phase and thee-phase displacement experiments on mixed-wet carbonate rocks. The three-phase WAG experiments were conducted on the same composite core at near-miscible reservoir condition; experiments differ in the injection order and length of their injection cycles. First, the two-phase water/oil and gas/oil displacement experiments and first cycles of WAG were used to estimate the two-phase relative permeabilities. Then, a history matching procedure over the full set of WAG cycles was carried out to tune the Larsen and Skauge WAG hysteresis model—namely the Land gas trapping parameter, the gas reduction exponent, the residual oil reduction factor and three-phase water relative permeability. The second part of this paper is dedicated to the value of information (VOI) analysis of the coreflood work program to assist the decision to proceed with a capital intensive WAG pilot at an offshore oilfield. Stochastic simulation of WAG injection using a fine scale sector model allowed to quantify the reduction in the range of uncertainty of key metrics—such as oil recovery, peak gas production and injectivity—linked with the additional SCAL information. The current study highlights the impact of the WAG injection sequence on the oil recovery and trapping mechanism. In addition, it is shown that the relative permeabilities and hysteresis model calibrated on one particular set of injection cycles fail to capture the WAG performance when the injection cycles are altered. Finally, the VOI methodology demonstrated the value enhancement from the coreflood work program.
{"title":"Experimental Study and History Match of Near-Miscible WAG Coreflood Experiments on Mixed-Wet Carbonate Rocks","authors":"M. E. El Faidouzi","doi":"10.2118/206307-ms","DOIUrl":"https://doi.org/10.2118/206307-ms","url":null,"abstract":"\u0000 Water-alternating-gas (WAG) injection, both miscible and immiscible, is a widely used enhanced oil recovery method with over 80 field cases. Despite its prevalence, the numerical modeling of the physical processes involved remains poorly understood, and existing models often lack predictability. Part of the complexity stems from the component exchange between gas and oil and the hysteretic relative permeability effects. Thus, improving the reliability of numerical models requires the calibration of the equation of state (EOS) against phase behavior data from swelling/extraction and slim-tube tests, and the calibration of the three-phase relative permeability model against WAG coreflood experiments.\u0000 This paper presents the results and interpretation of a complete set of two-phase and thee-phase displacement experiments on mixed-wet carbonate rocks. The three-phase WAG experiments were conducted on the same composite core at near-miscible reservoir condition; experiments differ in the injection order and length of their injection cycles.\u0000 First, the two-phase water/oil and gas/oil displacement experiments and first cycles of WAG were used to estimate the two-phase relative permeabilities. Then, a history matching procedure over the full set of WAG cycles was carried out to tune the Larsen and Skauge WAG hysteresis model—namely the Land gas trapping parameter, the gas reduction exponent, the residual oil reduction factor and three-phase water relative permeability.\u0000 The second part of this paper is dedicated to the value of information (VOI) analysis of the coreflood work program to assist the decision to proceed with a capital intensive WAG pilot at an offshore oilfield. Stochastic simulation of WAG injection using a fine scale sector model allowed to quantify the reduction in the range of uncertainty of key metrics—such as oil recovery, peak gas production and injectivity—linked with the additional SCAL information.\u0000 The current study highlights the impact of the WAG injection sequence on the oil recovery and trapping mechanism. In addition, it is shown that the relative permeabilities and hysteresis model calibrated on one particular set of injection cycles fail to capture the WAG performance when the injection cycles are altered. Finally, the VOI methodology demonstrated the value enhancement from the coreflood work program.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77046655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reservoir uncertainties, high water cut, completion integrity along with declining production are the major challenges of a mature field. These integrated with dying facilities and poor field production are key issues that each oil and gas company is facing these days. Arresting production decline is an inevitable objective, but with the existing techniques/steps involved, it becomes a cumbersome and exorbitant affair for the operators to meet their requirements. In addition, incompetent and flawed well data makes it more challenging to analyze mature fields. Although flow rate data is the most easily accessible data for mature fields, the absence of pressure data (flowing bottom-hole or wellhead pressure) remains a big obstacle for the application of conventional production enhancement and well screening strategies for most of the mature fields. A real-time optimization tool is thus constructed by developing a hybrid modelling technique that encapsulates Kriging and Fuzzy Logic to account for the imprecisions and uncertainties involved while identification of subsurface locations for production optimization of a mature field using only production data. The data from the existing wells in the field is used to generate a membership function based on its historical performance and productivity, thereby generating a spatial map of prospective areas, where secondary development operations can be taken up for production optimization.
{"title":"Unlocking Field Potential of Mature Fields Using Hybrid Fuzzy Modelling and Kriging Method","authors":"Saransh Surana","doi":"10.2118/208631-stu","DOIUrl":"https://doi.org/10.2118/208631-stu","url":null,"abstract":"\u0000 Reservoir uncertainties, high water cut, completion integrity along with declining production are the major challenges of a mature field. These integrated with dying facilities and poor field production are key issues that each oil and gas company is facing these days. Arresting production decline is an inevitable objective, but with the existing techniques/steps involved, it becomes a cumbersome and exorbitant affair for the operators to meet their requirements. In addition, incompetent and flawed well data makes it more challenging to analyze mature fields. Although flow rate data is the most easily accessible data for mature fields, the absence of pressure data (flowing bottom-hole or wellhead pressure) remains a big obstacle for the application of conventional production enhancement and well screening strategies for most of the mature fields.\u0000 A real-time optimization tool is thus constructed by developing a hybrid modelling technique that encapsulates Kriging and Fuzzy Logic to account for the imprecisions and uncertainties involved while identification of subsurface locations for production optimization of a mature field using only production data. The data from the existing wells in the field is used to generate a membership function based on its historical performance and productivity, thereby generating a spatial map of prospective areas, where secondary development operations can be taken up for production optimization.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76421908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Z. Fu, Kuan-liang Zhu, Lei Wang, Jing-yi Xu, Qian Wang, Jinzhong Wang, Lingling Wang, Yufei Liu
In oil and gas industry, it is inevitable that the developed reserve will gradually become exhausted. Under such circumstance, in order to stabilize oil production and meet increasing energy demand, we have no choice but to improve oil recovery from matured field as much as possible, since finding new large reservoir is quite hard in the future. For Jidong Oilfield in China, a lot of method can be used for improving oil production, one of which is deep pumping method by increasing pump setting depth, especially for depleted reservoir. Deep pumping method can be helpful to lower bottom hole pressure and enlarge drawdown pressure between producing layer and downhole. Not only can this method generate more power to displace oil from reservoir to well and subsequently increase oil drainage area, leading to higher oil recovery, but also can boost pump fillage and finally obtain high production efficiency. Even though, this method still brings many disadvantages. In Jidong Oilfield, we sometimes set the 1.5in pump at over 3000m depth (in this paper, all well related are rod pumping wells), where varied problems happened as followed:
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This paper describes the transformational application of Artificial Intelligence (AI) in Equinor's annual well planning and maturation process. Well planning is a complex decision-making process, like many other processes in the industry. There are thousands of choices, conflicting business drivers, lots of uncertainty, and hidden bias. These complexities all add up, which makes good decision making very hard. In this application, AI has been used for automated and unbiased evaluation of the full solution space, with the objective to optimize the selection of drilling campaigns while taking into account complex issues such as anti-collision with existing wells, drilling hazards and trade-offs between cost, value and risk. Designing drillable well trajectories involves a sequence of decisions, which makes the process very suitable for AI algorithms. Different solver architectures, or algorithms, can be used to play this game. This is similar to how companies such as Google-owned DeepMind develop customized solvers for games such as Go and StarCraft. The chosen method is a Tree Search algorithm with an evolutionary layer on top, providing a good balance in terms of performance (i.e., speed) vs. exploration capability (i.e., it looks "wide" in the option space). The algorithm has been deployed in a full stack web-based application that allows users to follow an end-2-end workflow: from defining well trajectory design rules and constraints to running the AI engine and evaluating results to the optimization of multi-well drilling campaigns based on risk, value and cost objectives. The full-size paper describes different Norwegian Continental Shelf (NCS) use cases of this AI assisted well trajectory planning. Results to-date indicate significant CAPEX savings potential and step-change improvements in decision speed (months to days) compared to routine manual workflows. There are very limited real transformative examples of Artificial Intelligence in multi- disciplinary workflows. This paper therefore gives a unique insight how a combination of data science, domain expertise and end user feedback can lead to powerful and transformative AI solutions – implemented at scale within an existing organization.
{"title":"Novel Application of Artificial Intelligence with Potential to Transform Well Planning Workflows on the Norwegian Continental Shelf","authors":"J. G. Vabø, E. Delaney, T. Savel, N. Dolle","doi":"10.2118/206339-ms","DOIUrl":"https://doi.org/10.2118/206339-ms","url":null,"abstract":"\u0000 This paper describes the transformational application of Artificial Intelligence (AI) in Equinor's annual well planning and maturation process.\u0000 Well planning is a complex decision-making process, like many other processes in the industry. There are thousands of choices, conflicting business drivers, lots of uncertainty, and hidden bias. These complexities all add up, which makes good decision making very hard.\u0000 In this application, AI has been used for automated and unbiased evaluation of the full solution space, with the objective to optimize the selection of drilling campaigns while taking into account complex issues such as anti-collision with existing wells, drilling hazards and trade-offs between cost, value and risk.\u0000 Designing drillable well trajectories involves a sequence of decisions, which makes the process very suitable for AI algorithms. Different solver architectures, or algorithms, can be used to play this game. This is similar to how companies such as Google-owned DeepMind develop customized solvers for games such as Go and StarCraft.\u0000 The chosen method is a Tree Search algorithm with an evolutionary layer on top, providing a good balance in terms of performance (i.e., speed) vs. exploration capability (i.e., it looks \"wide\" in the option space).\u0000 The algorithm has been deployed in a full stack web-based application that allows users to follow an end-2-end workflow: from defining well trajectory design rules and constraints to running the AI engine and evaluating results to the optimization of multi-well drilling campaigns based on risk, value and cost objectives.\u0000 The full-size paper describes different Norwegian Continental Shelf (NCS) use cases of this AI assisted well trajectory planning.\u0000 Results to-date indicate significant CAPEX savings potential and step-change improvements in decision speed (months to days) compared to routine manual workflows.\u0000 There are very limited real transformative examples of Artificial Intelligence in multi- disciplinary workflows. This paper therefore gives a unique insight how a combination of data science, domain expertise and end user feedback can lead to powerful and transformative AI solutions – implemented at scale within an existing organization.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76435652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The production and transportation of high viscosity liquid/gas two-phase along petroleum production system is a challenging operation due to the lack of understanding the flow behavior and characteristics. In particular, accurate prediction of two-phase slug length in pipes is crucial to efficiently operate and safely design oil well and separation facilities. The objective of this study is to develop a mechanistic model to predict high viscosity liquid slug length in pipelines and to optimize the proper set of closure relationships required to ensure high accuracy prediction. A large high viscosity liquid slug length database is collected and presented in this study, against which the proposed model is validated and compared with other models. A mechanistic slug length model is derived based on the first principles of mass and momentum balances over a two-phase slug unit, which requires a set of closure relationships of other slug characteristics. To select the proper set of closure relationships, a numerical optimization is carried out using a large slug length dataset to minimize the prediction error. Thousands of combinations of various slug flow closure relationships were evaluated to identify the most appropriate relationships for the proposed slug length model under high viscosity slug length condition. Results show that the proposed slug length mechanistic model is applicable for a wide range of liquid viscosities and is sensitive to the selected closure relationships. Results revealed that the optimum closure relationships combination is Archibong-Eso et al. (2018) for slug frequency, Malnes (1983) for slug liquid holdup, Jeyachandra et al. (2012) for drift velocity, and Nicklin et al. (1962) for the distribution coefficient. Using the above set of closure relationships, model validation yields 37.8% absolute average percent error, outperforming all existing slug length models.
{"title":"Prediction of High Viscosity Liquid/Gas Two-Phase Slug Length in Horizontal and Slightly Inclined Pipelines","authors":"O. Shaaban, E. Al-Safran","doi":"10.2118/206280-ms","DOIUrl":"https://doi.org/10.2118/206280-ms","url":null,"abstract":"\u0000 The production and transportation of high viscosity liquid/gas two-phase along petroleum production system is a challenging operation due to the lack of understanding the flow behavior and characteristics. In particular, accurate prediction of two-phase slug length in pipes is crucial to efficiently operate and safely design oil well and separation facilities. The objective of this study is to develop a mechanistic model to predict high viscosity liquid slug length in pipelines and to optimize the proper set of closure relationships required to ensure high accuracy prediction. A large high viscosity liquid slug length database is collected and presented in this study, against which the proposed model is validated and compared with other models. A mechanistic slug length model is derived based on the first principles of mass and momentum balances over a two-phase slug unit, which requires a set of closure relationships of other slug characteristics. To select the proper set of closure relationships, a numerical optimization is carried out using a large slug length dataset to minimize the prediction error. Thousands of combinations of various slug flow closure relationships were evaluated to identify the most appropriate relationships for the proposed slug length model under high viscosity slug length condition. Results show that the proposed slug length mechanistic model is applicable for a wide range of liquid viscosities and is sensitive to the selected closure relationships. Results revealed that the optimum closure relationships combination is Archibong-Eso et al. (2018) for slug frequency, Malnes (1983) for slug liquid holdup, Jeyachandra et al. (2012) for drift velocity, and Nicklin et al. (1962) for the distribution coefficient. Using the above set of closure relationships, model validation yields 37.8% absolute average percent error, outperforming all existing slug length models.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"168 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76641777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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