This paper address the numerical simulation of the chemically enhanced gas injection technology (ChEGas-EOR) at core and reservoir scales. In this technique, a liquid chemical solution, having engineered properties, is sprayed along with the gas stream. The mist travels through the wellbore and further introduced in the reservoir. Previous lab tests, pilot studies in light & intermediate oil reservoirs indicate that the application of CheGas-EOR allows for a reduction in operational costs, increases the chemical penetration radii and decreases the retention rate in the rock. However, the associated uncertainty is still too high to develop this process on a productive scale. In this work we use a developed phenomenological model to build a tool that assist in design and evaluation of Chemical Gas EOR operations aiming to reduce the uncertainties and optimize oil recovery. We developed a mathematical model, based on the most important transport and surface phenomena. Non-equilibrium mass transfer between phases during the interception of the chemical solution droplets with the liquid phases. Active chemical concentration in miscible liquid phases is much lower than liquid-based chemical injection opperations. As a consequence, dissolution and adsorption rate of active chemicals with reservoir rocks are slow. The model is base on the extended black-oil model formulation coupled to local mass balance equations of active chemicals. Non-equilibrium mass transfer processes are represented with interception, dissolution and a first order kinetic sorption models. The model was adjusted and then validated using experimental data from core-.floodint tests. Good agreement of the simulations results with experimental observations were obtained. The model can predict the relevant behavior of the disperse chemical injection in the gas phase in porous media. Also, well injections simulations at reservoir scale using the matched parameters from laboratory, reproduced pilot field results. Simulation experiments predict that the CheGasEOR process can increased substantially the oil recovery factor. For the first time, a model for disperse chemical injection for EOR applications is developed and validated at core and reservoir scale. The simulation model allows the evaluation of this technology at different scales. Therefore, it is possible to use it to optimize operating conditions and perform sensitivity analysis for field applications.
{"title":"Modelling Dispersed Chemical Droplets Injection in the Gas Stream for EOR Applications","authors":"J. Valencia, J. Mejía, A. Ocampo, A. Restrepo","doi":"10.2118/196620-ms","DOIUrl":"https://doi.org/10.2118/196620-ms","url":null,"abstract":"\u0000 This paper address the numerical simulation of the chemically enhanced gas injection technology (ChEGas-EOR) at core and reservoir scales. In this technique, a liquid chemical solution, having engineered properties, is sprayed along with the gas stream. The mist travels through the wellbore and further introduced in the reservoir. Previous lab tests, pilot studies in light & intermediate oil reservoirs indicate that the application of CheGas-EOR allows for a reduction in operational costs, increases the chemical penetration radii and decreases the retention rate in the rock. However, the associated uncertainty is still too high to develop this process on a productive scale. In this work we use a developed phenomenological model to build a tool that assist in design and evaluation of Chemical Gas EOR operations aiming to reduce the uncertainties and optimize oil recovery.\u0000 We developed a mathematical model, based on the most important transport and surface phenomena. Non-equilibrium mass transfer between phases during the interception of the chemical solution droplets with the liquid phases. Active chemical concentration in miscible liquid phases is much lower than liquid-based chemical injection opperations. As a consequence, dissolution and adsorption rate of active chemicals with reservoir rocks are slow. The model is base on the extended black-oil model formulation coupled to local mass balance equations of active chemicals. Non-equilibrium mass transfer processes are represented with interception, dissolution and a first order kinetic sorption models.\u0000 The model was adjusted and then validated using experimental data from core-.floodint tests. Good agreement of the simulations results with experimental observations were obtained. The model can predict the relevant behavior of the disperse chemical injection in the gas phase in porous media. Also, well injections simulations at reservoir scale using the matched parameters from laboratory, reproduced pilot field results. Simulation experiments predict that the CheGasEOR process can increased substantially the oil recovery factor.\u0000 For the first time, a model for disperse chemical injection for EOR applications is developed and validated at core and reservoir scale. The simulation model allows the evaluation of this technology at different scales. Therefore, it is possible to use it to optimize operating conditions and perform sensitivity analysis for field applications.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114975395","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 Yibal Khuff/Sudair reservoirs were discovered in 1977. The field contains both Non-Associated Gas in the Sudair & Lower Khuff reservoirs and Associated Gas with oil rims in the Upper Khuff reservoirs. The Upper and Lower Khuff hydrocarbons contain 2–3% H2S and 4–6% CO2, whereas the Sudair gas contain 1–1.5% CO2 and less than 50 ppm H2S. The Field Development Plan (FDP), a multibillion dollar sour development project, was completed in 2011 proposing a total of 47 wells, 34 dedicated horizontal/vertical wells for oil rim production and 13 commingled vertical/deviated gas wells, and the construction of new sour surface facilities with a gas production capacity of 6 MMm3/day. FDP execution started in 2016 while the details of field start-up, scheduled a few years later, were still being planned. As part of this planning, it was noticed that a number of pre-drilled wells required perforation and clean-up before facility startup. Due to the time necessary to prepare all the pre-drilled wells, pre-production wellbore cross-flow was expected to occur in wells located in the West block of the field. A dedicated subsurface team was assigned in 2017 to evaluate and mitigate the potential risks associated with this expected cross-flow through the wellbore resulting from the pressure difference between the Lower Khuff and Upper Khuff layers. This paper covers the integrated approach that the team followed to address the expected cross-flow issue, including: Basis for pre-production cross- flowThe quantification of the cross-flow using analytical and numerical simulation methodsThe assessment of the impact of cross-flow on process safety and the environment (i.e. drilling risks with potential blow out of sour gas) and social responsibility (i.e. production capacity and ultimate recovery losses resulting in lower benefits to the community)The identification and assessment of solutions to stop/reduce the cross-flowThe implementation of a robust and feasible mitigation plan The conducted study demonstrated that the impact of cross-flow at well level would be severe. The cross-flow rate could reach up to 25-137 Km3/day/well, while the field level cross-flow rate could reach up to 400 Km3/day. The oil rate capacity reduction in the West Block wells could reach 20-30% at start-up, resulting in a total only 1% oil ultimate recovery loss at field level since the West block contribution is small to total production and West block wells are constrained. The study also showed that the casing design is adequate and drilling risks are manageable even in case of cross-flow. Out of several solutions identified to stop/reduce cross-flow, phasing perforation was considered the most robust and feasible option. This paper presents the novel approach of a collaborative study that resulted in improved safety and reduced environmental risks and potential ultimate recovery losses. It also presents the methodologies used to allow the Assessment and Mitigation of Pre-Product
{"title":"A Collaborative Approach to Risk Assessment and Mitigation of Pre-Production Cross-Flow for a Multi-Billion Dollar Sour Field Development Project in the Sultanate of Oman: A Case Study","authors":"M. Cobanoglu, Abdullah Nabhani","doi":"10.2118/196707-ms","DOIUrl":"https://doi.org/10.2118/196707-ms","url":null,"abstract":"\u0000 The Yibal Khuff/Sudair reservoirs were discovered in 1977. The field contains both Non-Associated Gas in the Sudair & Lower Khuff reservoirs and Associated Gas with oil rims in the Upper Khuff reservoirs. The Upper and Lower Khuff hydrocarbons contain 2–3% H2S and 4–6% CO2, whereas the Sudair gas contain 1–1.5% CO2 and less than 50 ppm H2S. The Field Development Plan (FDP), a multibillion dollar sour development project, was completed in 2011 proposing a total of 47 wells, 34 dedicated horizontal/vertical wells for oil rim production and 13 commingled vertical/deviated gas wells, and the construction of new sour surface facilities with a gas production capacity of 6 MMm3/day.\u0000 FDP execution started in 2016 while the details of field start-up, scheduled a few years later, were still being planned. As part of this planning, it was noticed that a number of pre-drilled wells required perforation and clean-up before facility startup. Due to the time necessary to prepare all the pre-drilled wells, pre-production wellbore cross-flow was expected to occur in wells located in the West block of the field. A dedicated subsurface team was assigned in 2017 to evaluate and mitigate the potential risks associated with this expected cross-flow through the wellbore resulting from the pressure difference between the Lower Khuff and Upper Khuff layers.\u0000 This paper covers the integrated approach that the team followed to address the expected cross-flow issue, including: Basis for pre-production cross- flowThe quantification of the cross-flow using analytical and numerical simulation methodsThe assessment of the impact of cross-flow on process safety and the environment (i.e. drilling risks with potential blow out of sour gas) and social responsibility (i.e. production capacity and ultimate recovery losses resulting in lower benefits to the community)The identification and assessment of solutions to stop/reduce the cross-flowThe implementation of a robust and feasible mitigation plan\u0000 The conducted study demonstrated that the impact of cross-flow at well level would be severe. The cross-flow rate could reach up to 25-137 Km3/day/well, while the field level cross-flow rate could reach up to 400 Km3/day. The oil rate capacity reduction in the West Block wells could reach 20-30% at start-up, resulting in a total only 1% oil ultimate recovery loss at field level since the West block contribution is small to total production and West block wells are constrained. The study also showed that the casing design is adequate and drilling risks are manageable even in case of cross-flow. Out of several solutions identified to stop/reduce cross-flow, phasing perforation was considered the most robust and feasible option.\u0000 This paper presents the novel approach of a collaborative study that resulted in improved safety and reduced environmental risks and potential ultimate recovery losses. It also presents the methodologies used to allow the Assessment and Mitigation of Pre-Product","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115233014","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}
D. Macaluso, N. Colombi, L. Castelnuovo, M. Calderoni, P. Prevosti
Unexpected water accumulation (called perched water) can be present inside hydrocarbon bearing reservoirs. In case of limited or poor geophysical data, the prediction of this accumulation may be difficult. In this paper, a real case is used to show how the presence of perched water was initially supposed and then verified through production data analysis. During the development campaign of a deep water reservoir in West Africa, a water injector well found an unexpected shallower water table. To understand the nature of this water, the gas while drilling data of two oil producer drilled in the same area of the water injector were analysed. Based on this analysis the last meters of the open hole section of both oil producers were in water. The integration of gas while drilling data, stratigraphy, sedimentology and structural settings knowledge of the area suggested that this water was locally trapped during oil migration, most likely due to the presence of a structural barrier. The two oil producer wells, located in the supposed perched water area, were successfully started-up. The behavior of both wells was daily monitored to understand and confirm the nature of perched water phenomenon. From day one, the two wells showed water production. After few weeks, the water cut of one well clearly started to reduce. For the other well, the water cut behavior was constant and only after one year of production the declining trend was appreciated. The observed declining trend of water production was the final confirmation that aquifer in this sector of the field is isolated and with limited extension. The water cut trend was also captured in the 3D dynamic reservoir model. In addition, tracers were implemented in the model to identify different water production sources (injected or perched) and to forecast their evolution during the field life. The literature on perched water is quite limited and usually this kind of phenomenon is detected and described only on the geological side, but the production behavior of this water is rarely observed. This case study is integrating the geological and geophysical knowledge of the field with production data analysis to understand perched water behavior and can be considered a reference for other similar situation.
{"title":"Perched Water - Identification and Production Behavior In A Real Case","authors":"D. Macaluso, N. Colombi, L. Castelnuovo, M. Calderoni, P. Prevosti","doi":"10.2118/196635-ms","DOIUrl":"https://doi.org/10.2118/196635-ms","url":null,"abstract":"\u0000 Unexpected water accumulation (called perched water) can be present inside hydrocarbon bearing reservoirs. In case of limited or poor geophysical data, the prediction of this accumulation may be difficult.\u0000 In this paper, a real case is used to show how the presence of perched water was initially supposed and then verified through production data analysis.\u0000 During the development campaign of a deep water reservoir in West Africa, a water injector well found an unexpected shallower water table. To understand the nature of this water, the gas while drilling data of two oil producer drilled in the same area of the water injector were analysed. Based on this analysis the last meters of the open hole section of both oil producers were in water. The integration of gas while drilling data, stratigraphy, sedimentology and structural settings knowledge of the area suggested that this water was locally trapped during oil migration, most likely due to the presence of a structural barrier.\u0000 The two oil producer wells, located in the supposed perched water area, were successfully started-up. The behavior of both wells was daily monitored to understand and confirm the nature of perched water phenomenon. From day one, the two wells showed water production. After few weeks, the water cut of one well clearly started to reduce. For the other well, the water cut behavior was constant and only after one year of production the declining trend was appreciated. The observed declining trend of water production was the final confirmation that aquifer in this sector of the field is isolated and with limited extension. The water cut trend was also captured in the 3D dynamic reservoir model. In addition, tracers were implemented in the model to identify different water production sources (injected or perched) and to forecast their evolution during the field life.\u0000 The literature on perched water is quite limited and usually this kind of phenomenon is detected and described only on the geological side, but the production behavior of this water is rarely observed. This case study is integrating the geological and geophysical knowledge of the field with production data analysis to understand perched water behavior and can be considered a reference for other similar situation.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125485338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper summarizes the results of 3 years collaborative efforts of the Geophysicist, Production Geologist and Reservoir Engineers from the Astokh Development Team and the Geochemist from the LNG plant laboratory on integration of reservoir surveillance and reservoir modelling. In period 2015 - 2018 a large bulk of geological and field development data was collected in the Astokh field, in particular: cased and open hole logs, core, open hole pressure measurements, flowing and closed-in bottom hole pressures, well tests, new 4D seismic surveys (2015, 2018), fluid samples. Since 2016, essential progress was made in oil fingerprinting for oil production allocation. Simultaneously, the need for update of static and dynamic models was matured upon gaining experience in dynamic model history matching to field operational data (rates, pressures, results of well interventions). In other words, the need in update of geological architecture of the Astokh reservoir model was matured upon reaching critical mass of new data and experience. To revise well correlation, it was decided to combine different sorts of data, e.g. seismic, well logs and core data, reservoir pressures and oil fingerprinting. Different pressure regimes were identified for 3 layers within XXI reservoir. Pressure transient surveys were used for identification of geological boundaries where it's possible and this data was also incorporated into the model. Oil fingerprinting data was used for identification of different layers and compartments. Integration of pressure and oil geochemistry data allowed to identify inter-reservoir cross-flows caused by pressure differential. Based on all collected data, depositional model and reservoir correlation were updated based on sequential stratigraphy principles. As a result, a new static model of the main Astokh reservoirs was built, incorporating clinoform architecture for layers XXI-1’ and XXI-2. To check a new concept of geological architecture, material balance model was built and matched to the field data Integration of geological and field operational data provided a key to more advanced reservoir management and development strategy optimization. Based on updated reservoir model, new potential drilling targets were identified. Also, with new wells correlation, water flood optimization via management of voidage replacement ratio was proposed. The completed work suggests the essential improvement of reservoir modelling process by inclusion of the various well and reservoir surveillance data. This paper consists of the following sections: Introduction ∘ Field geology ∘ Field development history Scope of work complete and main results ∘ Proposed well logs correlation update for XXI-1’ and XXI-2 layers ▪ Integration of well logs, pressure and fluid analysis data ∘ Connectivity between layers XXI-S, XXI-1’ and XXI-2 ▪ Integration of pressure and oil fingerprinting data ∘ Connectivity within layers XXI-S, XXI-1’ and XXI-2 ▪ Results of pressur
{"title":"Improved Integrated Approach in Reservoir Modeling by the Example of the Astokh Field","authors":"D. Pavlov, N. Fedorov, O. Timofeeva, A. Vasiliev","doi":"10.2118/196719-ms","DOIUrl":"https://doi.org/10.2118/196719-ms","url":null,"abstract":"\u0000 This paper summarizes the results of 3 years collaborative efforts of the Geophysicist, Production Geologist and Reservoir Engineers from the Astokh Development Team and the Geochemist from the LNG plant laboratory on integration of reservoir surveillance and reservoir modelling.\u0000 In period 2015 - 2018 a large bulk of geological and field development data was collected in the Astokh field, in particular: cased and open hole logs, core, open hole pressure measurements, flowing and closed-in bottom hole pressures, well tests, new 4D seismic surveys (2015, 2018), fluid samples. Since 2016, essential progress was made in oil fingerprinting for oil production allocation. Simultaneously, the need for update of static and dynamic models was matured upon gaining experience in dynamic model history matching to field operational data (rates, pressures, results of well interventions). In other words, the need in update of geological architecture of the Astokh reservoir model was matured upon reaching critical mass of new data and experience. To revise well correlation, it was decided to combine different sorts of data, e.g. seismic, well logs and core data, reservoir pressures and oil fingerprinting. Different pressure regimes were identified for 3 layers within XXI reservoir. Pressure transient surveys were used for identification of geological boundaries where it's possible and this data was also incorporated into the model. Oil fingerprinting data was used for identification of different layers and compartments. Integration of pressure and oil geochemistry data allowed to identify inter-reservoir cross-flows caused by pressure differential. Based on all collected data, depositional model and reservoir correlation were updated based on sequential stratigraphy principles. As a result, a new static model of the main Astokh reservoirs was built, incorporating clinoform architecture for layers XXI-1’ and XXI-2. To check a new concept of geological architecture, material balance model was built and matched to the field data\u0000 Integration of geological and field operational data provided a key to more advanced reservoir management and development strategy optimization. Based on updated reservoir model, new potential drilling targets were identified. Also, with new wells correlation, water flood optimization via management of voidage replacement ratio was proposed. The completed work suggests the essential improvement of reservoir modelling process by inclusion of the various well and reservoir surveillance data.\u0000 This paper consists of the following sections:\u0000 Introduction ∘ Field geology ∘ Field development history Scope of work complete and main results ∘ Proposed well logs correlation update for XXI-1’ and XXI-2 layers ▪ Integration of well logs, pressure and fluid analysis data ∘ Connectivity between layers XXI-S, XXI-1’ and XXI-2 ▪ Integration of pressure and oil fingerprinting data ∘ Connectivity within layers XXI-S, XXI-1’ and XXI-2 ▪ Results of pressur","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122356580","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 particular challenge inherent to carbonate reservoirs is reservoir rock typing which impacts model initialisation and saturation distributions and hence STOIIP, phase mobilities, and flow behaviours. We explore how flow diagnostics can be used best to detect subtle differences in reservoir dynamics arising from different model initialisations by comparing flow diagnostics simulations with full-physics simulations. Flow diagnostics are applied to two reservoirs, a synthetic but realistic model representing an analogue for the Arab-D formation and a giant carbonate reservoir from the Middle East. Saturation modelling and reservoir rock typing is based on uniform and heterogeneous Pc and kr distributions, and further employs a state-of-the-art software that integrates of SCAL data and log-derived saturations. Sweep efficiency and dynamic Lorenz coefficients are then derived from the flow diagnostics results to quantify and compare the dynamic behaviour of the reservoir models. The full-physics simulations, which are used to validate the flow diagnostics results, are carried out with a commercial Black Oil simulator. The flow diagnostics results can clearly distinguish between different homogenous and heterogeneous rock-type distributions, wettability trends, as well as novel saturation modelling approaches that use dedicated software tools. Flow diagnostics capture the same trends in recovery predictions as the full-physics simulations. Importantly though, the total CPU time for a single flow diagnostics calculation including model loading is on the order of seconds, compared to minutes and hours for a single full-physics simulation. These observation give confidence that flow diagnostics can be used effectively to compare and contrast the impact of reservoir rock typing, saturation modelling, and model initialisation on reservoir performance before running full-physics simulations. Flow diagnostic hence allow us to reduce the number of reservoir models from a model ensemble and select a small number of diverse yet realistic reservoir models that capture the full range of geological uncertainties which are then subjected to more detailed reservoir simulation studies. Flow diagnostics are particularly well suited for complex carbonate reservoirs which are geologically more complex than clastic reservoirs and often exhibit significant uncertainties. Giant carbonate reservoirs are also challenging to simulate using full-physics simulators due to their size, so the impact of geological uncertainty on the predicted reservoir performance is often underexplored. Flow diagnostics are hence an effective complement to quantify uncertainty in state-of-the-art reservoir modelling, history matching and optimisation workflows, particularly for giant carbonate reservoirs.
{"title":"Using Flow Diagnostics to Quantify the Impact of Reservoir Rock Typing on Fluid Flow in Complex Carbonate Reservoirs","authors":"F. Alhashmi, S. Geiger, Mohamed AlBreiki","doi":"10.2118/196699-ms","DOIUrl":"https://doi.org/10.2118/196699-ms","url":null,"abstract":"\u0000 A particular challenge inherent to carbonate reservoirs is reservoir rock typing which impacts model initialisation and saturation distributions and hence STOIIP, phase mobilities, and flow behaviours. We explore how flow diagnostics can be used best to detect subtle differences in reservoir dynamics arising from different model initialisations by comparing flow diagnostics simulations with full-physics simulations.\u0000 Flow diagnostics are applied to two reservoirs, a synthetic but realistic model representing an analogue for the Arab-D formation and a giant carbonate reservoir from the Middle East. Saturation modelling and reservoir rock typing is based on uniform and heterogeneous Pc and kr distributions, and further employs a state-of-the-art software that integrates of SCAL data and log-derived saturations. Sweep efficiency and dynamic Lorenz coefficients are then derived from the flow diagnostics results to quantify and compare the dynamic behaviour of the reservoir models. The full-physics simulations, which are used to validate the flow diagnostics results, are carried out with a commercial Black Oil simulator.\u0000 The flow diagnostics results can clearly distinguish between different homogenous and heterogeneous rock-type distributions, wettability trends, as well as novel saturation modelling approaches that use dedicated software tools. Flow diagnostics capture the same trends in recovery predictions as the full-physics simulations. Importantly though, the total CPU time for a single flow diagnostics calculation including model loading is on the order of seconds, compared to minutes and hours for a single full-physics simulation. These observation give confidence that flow diagnostics can be used effectively to compare and contrast the impact of reservoir rock typing, saturation modelling, and model initialisation on reservoir performance before running full-physics simulations. Flow diagnostic hence allow us to reduce the number of reservoir models from a model ensemble and select a small number of diverse yet realistic reservoir models that capture the full range of geological uncertainties which are then subjected to more detailed reservoir simulation studies.\u0000 Flow diagnostics are particularly well suited for complex carbonate reservoirs which are geologically more complex than clastic reservoirs and often exhibit significant uncertainties. Giant carbonate reservoirs are also challenging to simulate using full-physics simulators due to their size, so the impact of geological uncertainty on the predicted reservoir performance is often underexplored. Flow diagnostics are hence an effective complement to quantify uncertainty in state-of-the-art reservoir modelling, history matching and optimisation workflows, particularly for giant carbonate reservoirs.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129519254","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}
Even though coring of rocks is the best way to characterize reservoir and source rocks geologically and petrophysically, this method is considered expensive, having a relatively high cost per foot. Alternatively, side-wall cores and cuttings are widely used in reservoir characterization at a relatively low cost. However, this method has limitations related to cuttings bad physical conditions, size, mixed lithological and mineralogical characteristics which make the commonly used conventional evaluation methods not applicable. This study introduces a robust combination of digital and conventional core analysis methods to overcome these limitations and characterize reservoir and shale cuttings derived from two hydrocarbon-bearing formations in New Zealand. Initially, all cuttings from both formations were screened based on their cutting sizes and later based on the visually observed textures using the stereomicroscope. This helped in selecting representative cuttings for the main identified textures. These cuttings were CT imaged at a resolution ranging from 40 to 4 microns/voxel resolution in order to confirm their rock textures and sedimentary structures for better characterization results. Next, mercury injection capillary pressure (MICP), X-ray diffraction (XRD), and petrographical analysis were conducted on all selected cuttings with different rock textures in order to understand the pore types, textural variations, diagenetic overprints and mineralogy of the cuttings samples. Then, they were scanned at optimum resolutions using Micro CT and 3D FIB-SEM microscopies. Finally, all acquired images were segmented digitally and 3D rock volumes were created. These volumes were used in computing porosity, permeability, formation factor resistivity (FRF) and poroperm trends digitally using numerical simulation techniques. Conventional and digital rock analysis showed that the cuttings derived from the reservoir interval are composed of an argillaceous sandstone with a very good computed porosity (18% up to 31%) and permeability (30 to 200 mD). On the other hand, the cuttings derived from the shale source rock interval, which were predominately composed of clay minerals, have a computed porosity of 12% to 13% (mainly inorganic pores) and an absolute permeability in the range of 0.5 to 4 Micro-Darcy. The digital poroperm trend analysis identified distinct poroperm trends for each formation which helped in understanding their petrophysical aspects. This integration between conventional and digital methods provided better geological and petrophysical understanding of both formations using a limited number of cuttings, less cost and time.
{"title":"Robust Characterisation Methods of Cuttings Derived from Siliciclastic Reservoir and Seal Rocks - A Case Study from New Zealand","authors":"O. Aljallad, S GraderAbraham, S. Koronfol","doi":"10.2118/196616-ms","DOIUrl":"https://doi.org/10.2118/196616-ms","url":null,"abstract":"\u0000 Even though coring of rocks is the best way to characterize reservoir and source rocks geologically and petrophysically, this method is considered expensive, having a relatively high cost per foot. Alternatively, side-wall cores and cuttings are widely used in reservoir characterization at a relatively low cost. However, this method has limitations related to cuttings bad physical conditions, size, mixed lithological and mineralogical characteristics which make the commonly used conventional evaluation methods not applicable. This study introduces a robust combination of digital and conventional core analysis methods to overcome these limitations and characterize reservoir and shale cuttings derived from two hydrocarbon-bearing formations in New Zealand.\u0000 Initially, all cuttings from both formations were screened based on their cutting sizes and later based on the visually observed textures using the stereomicroscope. This helped in selecting representative cuttings for the main identified textures. These cuttings were CT imaged at a resolution ranging from 40 to 4 microns/voxel resolution in order to confirm their rock textures and sedimentary structures for better characterization results. Next, mercury injection capillary pressure (MICP), X-ray diffraction (XRD), and petrographical analysis were conducted on all selected cuttings with different rock textures in order to understand the pore types, textural variations, diagenetic overprints and mineralogy of the cuttings samples. Then, they were scanned at optimum resolutions using Micro CT and 3D FIB-SEM microscopies. Finally, all acquired images were segmented digitally and 3D rock volumes were created. These volumes were used in computing porosity, permeability, formation factor resistivity (FRF) and poroperm trends digitally using numerical simulation techniques.\u0000 Conventional and digital rock analysis showed that the cuttings derived from the reservoir interval are composed of an argillaceous sandstone with a very good computed porosity (18% up to 31%) and permeability (30 to 200 mD). On the other hand, the cuttings derived from the shale source rock interval, which were predominately composed of clay minerals, have a computed porosity of 12% to 13% (mainly inorganic pores) and an absolute permeability in the range of 0.5 to 4 Micro-Darcy. The digital poroperm trend analysis identified distinct poroperm trends for each formation which helped in understanding their petrophysical aspects.\u0000 This integration between conventional and digital methods provided better geological and petrophysical understanding of both formations using a limited number of cuttings, less cost and time.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132047068","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}
Production optimization is the method for seeking the best possible well control and schedule plans in order to enhance reservoir performance under a given state and economic constraints. Determining the optimal injection and production control strategies through adjoint gradient-based optimization is a well-known practice in today’s modern reservoir management. However, apt handling of nonlinear control inputs, state and output constraints can be quite tedious with effects on the computational efficiency of the optimization algorithms used in practical production optimal control problems. In this paper, we develop an adjoint based interior-point inexact trust filter sequential quadratic programming (IITRF-SQP) method for solving constrained production optimization problems. Inexact trust-region is an extension of a filter trust region approach, which is used when the control input constraints Jacobians are of high dimension and are expensive to compute. The output constraints are handled using an interior-point method called- modified barrier-augmented Lagrangian, in which inequality constraints are treated by a modified barrier term and equality constraints with augmented Lagrangian terms. The algorithm we present uses the approximate information of Jacobians achieved through composite-step computation, which eliminates the cost of direct calculation of Jacobians and Hessians (gradients). The gradient information that provides criticality measure of the objective function is calculated using the adjoint method. Two numerical experiments on optimal water-flooding are presented. Performance comparisons of the proposed IITRF-SQP method with Lagrangian barrier method and sequential linear quadratic programming (SLQP) for solving production optimization problem are carried out. Results indicate that the gradient-based adjoint coupled with IITRF-SQP was able to improve net present value (NPV) through optimal production profiles with better computational efficacy via reduced convergence time and number of gradient and objective function evaluations.
{"title":"An Adjoint Inexact Trust Region Method for Nonlinear Constraint Production Optimization","authors":"Chithra Chakra, M. A. Kobaisi","doi":"10.2118/196666-ms","DOIUrl":"https://doi.org/10.2118/196666-ms","url":null,"abstract":"\u0000 Production optimization is the method for seeking the best possible well control and schedule plans in order to enhance reservoir performance under a given state and economic constraints. Determining the optimal injection and production control strategies through adjoint gradient-based optimization is a well-known practice in today’s modern reservoir management. However, apt handling of nonlinear control inputs, state and output constraints can be quite tedious with effects on the computational efficiency of the optimization algorithms used in practical production optimal control problems. In this paper, we develop an adjoint based interior-point inexact trust filter sequential quadratic programming (IITRF-SQP) method for solving constrained production optimization problems. Inexact trust-region is an extension of a filter trust region approach, which is used when the control input constraints Jacobians are of high dimension and are expensive to compute. The output constraints are handled using an interior-point method called- modified barrier-augmented Lagrangian, in which inequality constraints are treated by a modified barrier term and equality constraints with augmented Lagrangian terms. The algorithm we present uses the approximate information of Jacobians achieved through composite-step computation, which eliminates the cost of direct calculation of Jacobians and Hessians (gradients). The gradient information that provides criticality measure of the objective function is calculated using the adjoint method. Two numerical experiments on optimal water-flooding are presented. Performance comparisons of the proposed IITRF-SQP method with Lagrangian barrier method and sequential linear quadratic programming (SLQP) for solving production optimization problem are carried out. Results indicate that the gradient-based adjoint coupled with IITRF-SQP was able to improve net present value (NPV) through optimal production profiles with better computational efficacy via reduced convergence time and number of gradient and objective function evaluations.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115849366","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}
There are a vast number of reservoirs with drill cuttings and core images that have classification problems associated with them. This could be due to the images not being classified in the first place, or the images may be available but the interpretation reports could be missing. Another problem is that images from different wells could be interpreted by different wellsite geologists/sedimentologists and hence result in an inconsistent classification scheme. Finally, there could also be the problem of some images being incorrectly classified. Ergo it would be desirable to have an unbiased objective system that could overcome all of these issues. Step in convolutional neural networks. Advances during this decade in using convolutional neural networks for visual recognition of discriminately different objects means that now object recognition can be achieved to a significant extent. Once the network is trained on a representative set of lithological classes, then such a system just needs to be fed the raw drill cuttings or core images that it has not seen before and it will automatically assign a lithological class to each image and an associated probability of the image belonging to that class. In so doing, images below a certain probability threshold can be automatically flagged for further human investigation. The benefit of such a system would be to improve reservoir understanding by having all available images classified in a consistent manner hence keeping the characterization consistent as well. It would further help to reduce the time taken to get human expertise to complete the task, as well as the associated cost.
{"title":"Visual Recognition of Drill Cuttings Lithologies Using Convolutional Neural Networks to Aid Reservoir Characterisation","authors":"M. Kathrada, B. J. Adillah","doi":"10.2118/196675-ms","DOIUrl":"https://doi.org/10.2118/196675-ms","url":null,"abstract":"\u0000 There are a vast number of reservoirs with drill cuttings and core images that have classification problems associated with them. This could be due to the images not being classified in the first place, or the images may be available but the interpretation reports could be missing. Another problem is that images from different wells could be interpreted by different wellsite geologists/sedimentologists and hence result in an inconsistent classification scheme. Finally, there could also be the problem of some images being incorrectly classified. Ergo it would be desirable to have an unbiased objective system that could overcome all of these issues. Step in convolutional neural networks. Advances during this decade in using convolutional neural networks for visual recognition of discriminately different objects means that now object recognition can be achieved to a significant extent. Once the network is trained on a representative set of lithological classes, then such a system just needs to be fed the raw drill cuttings or core images that it has not seen before and it will automatically assign a lithological class to each image and an associated probability of the image belonging to that class. In so doing, images below a certain probability threshold can be automatically flagged for further human investigation. The benefit of such a system would be to improve reservoir understanding by having all available images classified in a consistent manner hence keeping the characterization consistent as well. It would further help to reduce the time taken to get human expertise to complete the task, as well as the associated cost.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"213 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124189051","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 this work we discuss the successful application of our previously developed automated scenario reduction approach applied to life-cycle optimization of a real field case. The inherent uncertainty present in the description of reservoir properties motivates the use of an ensemble of model scenarios to achieve an optimized robust reservoir development strategy. In order to accurately span the range of uncertainties it is imperative to build a relatively large ensemble of model scenarios. The size of the ensemble is directly proportional to the computational effort required in robust optimization. For high-dimensional, complex field case models this implies that a large ensemble of model scenarios which albeit accurately captures the inherent uncertainties would be computationally infeasible to be utilized for robust optimization. One of the ways to circumvent this problem is to work with a reduced subset of model scenarios. Methods based on heuristics and ad-hoc rules exist to select this reduced subset. However, in most of the cases, the optimal number of model realizations must be known upfront. Excessively small number of realizations may result in a subset that does not always capture the span of uncertainties present, leading to sub-optimal optimization results. This raises the question on how to effectively select a subset that contains an optimal number of realizations which both is able to capture the uncertainties present and allow for a computationally efficient robust optimization. To answer this question we have developed an automated framework to select the reduced ensemble which has been applied to an original ensemble of 300 equiprobable model scenarios of a real field case. The methodology relies on the fact that, ideally, the distance between the cumulative distribution functions (CDF) of the objective function (OF) of the full and reduced ensembles should be minimal. This allows the method to determine the smallest subset of realizations that both spans the range of uncertainties and provides an OF CDF that is representative of the full ensemble based on a statistical metric. In this real field case application we optimize the injection rates throughout the assets life-cycle with expected cumulative oil production as the OF. The newly developed framework selected a small subset of 17 model scenarios out of the original ensemble which was used for robust optimization. The optimal injection strategy achieved an average increase of 6% in cumulative oil production with a significant reduction, approximately 90%, in the computational effort. Validation of this optimal strategy over the original ensemble lead to very similar improvements in cumulative oil production, highlighting the reliability and accuracy of our framework.
{"title":"Production Optimisation Under Uncertainty with Automated Scenario Reduction: A Real-Field Case Application","authors":"E. Barros, R. Fonseca, R. J. Moraes","doi":"10.2118/196637-ms","DOIUrl":"https://doi.org/10.2118/196637-ms","url":null,"abstract":"\u0000 In this work we discuss the successful application of our previously developed automated scenario reduction approach applied to life-cycle optimization of a real field case. The inherent uncertainty present in the description of reservoir properties motivates the use of an ensemble of model scenarios to achieve an optimized robust reservoir development strategy. In order to accurately span the range of uncertainties it is imperative to build a relatively large ensemble of model scenarios. The size of the ensemble is directly proportional to the computational effort required in robust optimization. For high-dimensional, complex field case models this implies that a large ensemble of model scenarios which albeit accurately captures the inherent uncertainties would be computationally infeasible to be utilized for robust optimization. One of the ways to circumvent this problem is to work with a reduced subset of model scenarios. Methods based on heuristics and ad-hoc rules exist to select this reduced subset. However, in most of the cases, the optimal number of model realizations must be known upfront. Excessively small number of realizations may result in a subset that does not always capture the span of uncertainties present, leading to sub-optimal optimization results. This raises the question on how to effectively select a subset that contains an optimal number of realizations which both is able to capture the uncertainties present and allow for a computationally efficient robust optimization. To answer this question we have developed an automated framework to select the reduced ensemble which has been applied to an original ensemble of 300 equiprobable model scenarios of a real field case. The methodology relies on the fact that, ideally, the distance between the cumulative distribution functions (CDF) of the objective function (OF) of the full and reduced ensembles should be minimal. This allows the method to determine the smallest subset of realizations that both spans the range of uncertainties and provides an OF CDF that is representative of the full ensemble based on a statistical metric. In this real field case application we optimize the injection rates throughout the assets life-cycle with expected cumulative oil production as the OF. The newly developed framework selected a small subset of 17 model scenarios out of the original ensemble which was used for robust optimization. The optimal injection strategy achieved an average increase of 6% in cumulative oil production with a significant reduction, approximately 90%, in the computational effort. Validation of this optimal strategy over the original ensemble lead to very similar improvements in cumulative oil production, highlighting the reliability and accuracy of our framework.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129780774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Kelkouli, Hedi Hadj Arab, N. Amor, K. Kecili, N. Mokhtari
This paper has an objective of identifying the nature of formation fluid from an extreme tight fractured reservoir. A good understanding of petrophysical properties of the reservoir rock as well as the fluid it contains constitutes a real challenge for tight reservoirs, that are the most common unconventional sources of hydrocarbons. The front-line characterization mean is the Wireline logging which comes directly after drilling the well or while drilling, knowing that for low to extreme low porosity-permeability reservoirs any attempt of conventional well testing will not bring any added value not rather than a confirmation of reservoir tightness. A tailored workflow was adopted to design the most appropriate formation testing module, select the best depths for fluid sampling, and distinguish hydrocarbon from water bearing intervals. This workflow involves ultra-sonic and Electric Borehole Images in combination with Sonic Scanner for natural fractures detection, localization and characterization, integrating Dielectric recording and processing for petrophysical evaluation, then Formation Testing was carried out for fluid identification and sampling. The use of borehole electric and sonic imager coupled with advanced sonic acquisition helped not only to identify the natural fractures depths, but also the nature of these fractures. This integration was used for selecting the sampling station. Successful fluid sampling was carried out in 4 different depths (2 gas and 2 water), then a dielectric measurement was integrated to map the continuity of the water zone and narrow the uncertainty on fluid contact. This novel multidisciplinary approach that was adopted, integrates answer products from different domains to enable the interpreter, (the reservoir engineer, the geologist, and the Petrophysicist) to better understand and characterize the reservoir, toward a good reserve’s evaluation and appropriate development plan.
{"title":"Multidisciplinary Approach for Unconventional Reservoirs Characterisation by Integrating Wireline Openhole Logging Techniques – Electric and Sonic to Formation Testing","authors":"M. Kelkouli, Hedi Hadj Arab, N. Amor, K. Kecili, N. Mokhtari","doi":"10.2118/196689-ms","DOIUrl":"https://doi.org/10.2118/196689-ms","url":null,"abstract":"\u0000 This paper has an objective of identifying the nature of formation fluid from an extreme tight fractured reservoir. A good understanding of petrophysical properties of the reservoir rock as well as the fluid it contains constitutes a real challenge for tight reservoirs, that are the most common unconventional sources of hydrocarbons.\u0000 The front-line characterization mean is the Wireline logging which comes directly after drilling the well or while drilling, knowing that for low to extreme low porosity-permeability reservoirs any attempt of conventional well testing will not bring any added value not rather than a confirmation of reservoir tightness.\u0000 A tailored workflow was adopted to design the most appropriate formation testing module, select the best depths for fluid sampling, and distinguish hydrocarbon from water bearing intervals.\u0000 This workflow involves ultra-sonic and Electric Borehole Images in combination with Sonic Scanner for natural fractures detection, localization and characterization, integrating Dielectric recording and processing for petrophysical evaluation, then Formation Testing was carried out for fluid identification and sampling.\u0000 The use of borehole electric and sonic imager coupled with advanced sonic acquisition helped not only to identify the natural fractures depths, but also the nature of these fractures. This integration was used for selecting the sampling station. Successful fluid sampling was carried out in 4 different depths (2 gas and 2 water), then a dielectric measurement was integrated to map the continuity of the water zone and narrow the uncertainty on fluid contact.\u0000 This novel multidisciplinary approach that was adopted, integrates answer products from different domains to enable the interpreter, (the reservoir engineer, the geologist, and the Petrophysicist) to better understand and characterize the reservoir, toward a good reserve’s evaluation and appropriate development plan.","PeriodicalId":354509,"journal":{"name":"Day 3 Thu, September 19, 2019","volume":"367 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115276637","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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