Pub Date : 2021-05-17DOI: 10.30632/SPWLA-2021-0118
E. Acosta, Bhagwanpersad Nandlal, R. Harripersad
This research proposed an alternative method for determining the saturation exponent (n) by finding the best correlations for the heterogeneity index using available core data and considering wettability changes. The log curves of the variable n were estimated, and the effect on the water saturation (Sw) calculations and the Stock Tank Oil Initially In Place (STOIIP) in the Tambaredjo (TAM) oil field was analyzed. Core data were employed to obtain the relationship between n and heterogeneity using cross-plots against several heterogeneity indices, reservoir properties, and pore throat size. After filtering the data, the clay volume (Vcl), shale volume, silt volume, basic petrophysical property index (BPPI), net reservoir index, pore grain volume ratio, and rock texture were defined as the best matches. Their modified/improved equations were applied to the log data and evaluated. The n related to Vcl was the best selection based on the criteria of depth variations and logical responses to the lithology. The Sw model in this field showed certain log readings (high resistivity [Rt] reading ≥ 500 ohm.m) that infer these intervals to be probable inverse-wet (oil-wet). The cross-plots (Rt vs. Vcl; Rt vs. density [RHOB]; Rt vs. total porosity [PHIT]) were used to discard the lithologies related to a high Rt (e.g., lignites and calcareous rocks) and to correct Sw when these resulted in values below the estimated irreducible water saturation (Swir). The Sw calculations using the Indonesian equation were updated to incorporate n as a variable (log curves), comparing it with Sw from the core data and previous calculations using a fixed average value (n = 1.82) from the core data. An integrated approach was used to determine n, which is related to the reservoir’s heterogeneity and wettability changes. The values of n for high Rt (n > 2) intervals ranged from 2.3 to 8.5, which is not close to the field average n value (1.82). Specific correlations were found by discriminating Swir (Swir < 15%), (Swir 15%–19%), and Swir (> 19%). The results showed that using n as a variable parameter improved Sw from 39.5% to 36.5% average in the T1 and T2 sands, showing a better fit than the core data average and increasing the STOIIP estimations by 6.81%. This represents now a primary oil recovery of 12.1%, closer to the expected value for these reservoirs. Although many studies have been done on n determination and its effect on Sw calculations, using average values over a whole field is still a common practice regardless of heterogeneity and wettability considerations. This study proposed a method to include the formation of heterogeneity and wettability changes in n determination, allowing a more reliable Sw determination as demonstrated in the TAM oil field in Suriname.
{"title":"SATURATION EXPONENT AS A FUNCTION OF RESERVOIR HETEROGENEITY AND WETTABILITY IN THE TAMBAREDJO OIL FIELD, SURINAME","authors":"E. Acosta, Bhagwanpersad Nandlal, R. Harripersad","doi":"10.30632/SPWLA-2021-0118","DOIUrl":"https://doi.org/10.30632/SPWLA-2021-0118","url":null,"abstract":"This research proposed an alternative method for determining the saturation exponent (n) by finding the best correlations for the heterogeneity index using available core data and considering wettability changes. The log curves of the variable n were estimated, and the effect on the water saturation (Sw) calculations and the Stock Tank Oil Initially In Place (STOIIP) in the Tambaredjo (TAM) oil field was analyzed. Core data were employed to obtain the relationship between n and heterogeneity using cross-plots against several heterogeneity indices, reservoir properties, and pore throat size. After filtering the data, the clay volume (Vcl), shale volume, silt volume, basic petrophysical property index (BPPI), net reservoir index, pore grain volume ratio, and rock texture were defined as the best matches. Their modified/improved equations were applied to the log data and evaluated. The n related to Vcl was the best selection based on the criteria of depth variations and logical responses to the lithology. The Sw model in this field showed certain log readings (high resistivity [Rt] reading ≥ 500 ohm.m) that infer these intervals to be probable inverse-wet (oil-wet). The cross-plots (Rt vs. Vcl; Rt vs. density [RHOB]; Rt vs. total porosity [PHIT]) were used to discard the lithologies related to a high Rt (e.g., lignites and calcareous rocks) and to correct Sw when these resulted in values below the estimated irreducible water saturation (Swir). The Sw calculations using the Indonesian equation were updated to incorporate n as a variable (log curves), comparing it with Sw from the core data and previous calculations using a fixed average value (n = 1.82) from the core data. An integrated approach was used to determine n, which is related to the reservoir’s heterogeneity and wettability changes. The values of n for high Rt (n > 2) intervals ranged from 2.3 to 8.5, which is not close to the field average n value (1.82). Specific correlations were found by discriminating Swir (Swir < 15%), (Swir 15%–19%), and Swir (> 19%). The results showed that using n as a variable parameter improved Sw from 39.5% to 36.5% average in the T1 and T2 sands, showing a better fit than the core data average and increasing the STOIIP estimations by 6.81%. This represents now a primary oil recovery of 12.1%, closer to the expected value for these reservoirs. Although many studies have been done on n determination and its effect on Sw calculations, using average values over a whole field is still a common practice regardless of heterogeneity and wettability considerations. This study proposed a method to include the formation of heterogeneity and wettability changes in n determination, allowing a more reliable Sw determination as demonstrated in the TAM oil field in Suriname.","PeriodicalId":153712,"journal":{"name":"SPWLA 62nd Annual Online Symposium Transactions","volume":"456 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125799716","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}
Pub Date : 2021-05-17DOI: 10.30632/SPWLA-2021-0107
Yon Blanco, Ben Fletcher, R. Webber, Alistair Maguire, Velerian S. Lopes
Reservoir management utilizes time-lapse pressure data that is captured over years in order to monitor reservoir development. Several methods can be used to establish field-wide hydraulic lateral and/or vertical connectivity: well testing, monitoring of permanent downhole gauges, wireline and LWD formation testers. While a typical formation pressure survey provides information about reservoir depletion or charge (production or injection), in a field with several wells it is not clearly understood where the pressure disturbances are coming from, which can hamper further field development decision making in terms of infill well selection and drilling. A novel method is introduced where a Formation Pressure While Drilling (FPWD) tool is run in UKCS wells and used to acquire interference data while drilling. Initially reservoir pressures are acquired as soon as practically possible after drilling. Having established these benchmark pressures, nearby injectors and/or producers can be started or shut in one at a time. Drilling is then resumed and after a certain time has elapsed since the benchmark pressure acquisition (typically at least 12 hours), the pressure measurements are repeated using the FPWD tool to evaluate the influence of the created transients in order to prove or disprove either lateral or vertical hydraulic connectivity across reservoirs. This way, the influence of a single offset well is evaluated in real time over the reservoir being currently drilled. This helps in the determination of interference pattern whereby injector wells can be judged for selective zone injections and producers can be rated in terms of zonal contribution which can help in completion design. These direct pressure measurements can illuminate reservoir pressure complexity seen in mature fields and provide operators with the means to safely and effectively construct wells to develop brownfields. The pressure changes obtained are used not only by reservoir engineers as an additional source of dynamic data into the reservoir simulation model but also help geologists in refining the geological or basin model. Two applications of real-time interference testing using FPWD from a recent drilling campaign are shown. In the first application, communication between wells is tested to reduce the risk of accidentally completing a well in an area of the field that experiences insufficient injection support. In the second application, real-time interference testing is used to identify a specific zone in a multi-layered reservoir sequence in order to enable selective completion.
{"title":"FIELDWIDE DYNAMIC PRESSURE SURVEILLANCE WITH FPWD TECHNOLOGY","authors":"Yon Blanco, Ben Fletcher, R. Webber, Alistair Maguire, Velerian S. Lopes","doi":"10.30632/SPWLA-2021-0107","DOIUrl":"https://doi.org/10.30632/SPWLA-2021-0107","url":null,"abstract":"Reservoir management utilizes time-lapse pressure data that is captured over years in order to monitor reservoir development. Several methods can be used to establish field-wide hydraulic lateral and/or vertical connectivity: well testing, monitoring of permanent downhole gauges, wireline and LWD formation testers. While a typical formation pressure survey provides information about reservoir depletion or charge (production or injection), in a field with several wells it is not clearly understood where the pressure disturbances are coming from, which can hamper further field development decision making in terms of infill well selection and drilling. A novel method is introduced where a Formation Pressure While Drilling (FPWD) tool is run in UKCS wells and used to acquire interference data while drilling. Initially reservoir pressures are acquired as soon as practically possible after drilling. Having established these benchmark pressures, nearby injectors and/or producers can be started or shut in one at a time. Drilling is then resumed and after a certain time has elapsed since the benchmark pressure acquisition (typically at least 12 hours), the pressure measurements are repeated using the FPWD tool to evaluate the influence of the created transients in order to prove or disprove either lateral or vertical hydraulic connectivity across reservoirs. This way, the influence of a single offset well is evaluated in real time over the reservoir being currently drilled. This helps in the determination of interference pattern whereby injector wells can be judged for selective zone injections and producers can be rated in terms of zonal contribution which can help in completion design. These direct pressure measurements can illuminate reservoir pressure complexity seen in mature fields and provide operators with the means to safely and effectively construct wells to develop brownfields. The pressure changes obtained are used not only by reservoir engineers as an additional source of dynamic data into the reservoir simulation model but also help geologists in refining the geological or basin model. Two applications of real-time interference testing using FPWD from a recent drilling campaign are shown. In the first application, communication between wells is tested to reduce the risk of accidentally completing a well in an area of the field that experiences insufficient injection support. In the second application, real-time interference testing is used to identify a specific zone in a multi-layered reservoir sequence in order to enable selective completion.","PeriodicalId":153712,"journal":{"name":"SPWLA 62nd Annual Online Symposium Transactions","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125251116","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}
Pub Date : 2021-05-17DOI: 10.30632/SPWLA-2021-0013
M. Mohammadlou, M. G. Reppert, Roxane Del Negro, George Jones
During well planning, drillers and petrophysicists have different principle objectives. The petrophysicist’s aim is to acquire critical well data, but this can lead to increased operational risk. The driller is focused on optimizing the well design, which can result in compromised data quality. In extreme cases, the impact of well design on petrophysical data can lead to erroneous post-well results that impact the entire value-chain assessment and decision making toward field development. In this paper, we present a case study from a syn-rift, Upper Jurassic reservoir in the Norwegian Sea where well design significantly impacted reservoir characterization. Three wells (exploration, appraisal, and geopilot) are compared in order to demonstrate the impact of overbalanced drilling on well data from both logs and core. Implications for reservoir quality assessment, volume estimates, and the errors introduced into both a static geomodel and dynamic reservoir simulation are discussed. This case study highlights the importance of optimizing well design for petrophysical data collection and demonstrates the potential for value creation. Extensive data collection was initially carried out in both exploration and appraisal wells, including full sets of logging while drilling (LWD), wireline logging, fluid sampling, and extensive coring. Both wells were drilled with considerable overbalanced mud weights due to the risk of overpressured reservoirs in the region. The log data was subsequently corrected for significant mud-filtration invasion, with calibration to core measurements guiding the interpretation. Geological and reservoir models were built based on results from the two wells, and development wells were planned accordingly. A thorough investigation of core material raised suspicion that there could also be a significant adverse effect of core properties resulting from overbalanced drilling. The implications were so significant for the reservoir volume that a strategic decision was made to drill a geopilot well close to the initial exploration well, prior to field development drilling. The well was drilled six years after the initial exploration phase with considerably lower overbalance. Extensive well data, including one core, were acquired. The recovered core was crucial in order to compare the reservoir properties for comparable facies between all three wells. The results from the core demonstrate distinctly different rock quality characteristics, especially at the high end of the reservoir quality spectrum. Results of the core study confirmed the initial hypothesis that overbalanced drilling had significantly impacted the properties of the core as well as the well logs. The study concluded that the updated reservoir model properties would significantly increase the in-place volumes compared to the pre-geopilot estimate. This study shows how well design adversely affected petrophysical measurements and how errors in these data compromised geologica
{"title":"THE IMPACT OF OVERBALANCED DRILLING FROM EXPLORATION/APPRAISAL WELLS TO FIELD DEVELOPMENT PLAN","authors":"M. Mohammadlou, M. G. Reppert, Roxane Del Negro, George Jones","doi":"10.30632/SPWLA-2021-0013","DOIUrl":"https://doi.org/10.30632/SPWLA-2021-0013","url":null,"abstract":"During well planning, drillers and petrophysicists have different principle objectives. The petrophysicist’s aim is to acquire critical well data, but this can lead to increased operational risk. The driller is focused on optimizing the well design, which can result in compromised data quality. In extreme cases, the impact of well design on petrophysical data can lead to erroneous post-well results that impact the entire value-chain assessment and decision making toward field development. In this paper, we present a case study from a syn-rift, Upper Jurassic reservoir in the Norwegian Sea where well design significantly impacted reservoir characterization. Three wells (exploration, appraisal, and geopilot) are compared in order to demonstrate the impact of overbalanced drilling on well data from both logs and core. Implications for reservoir quality assessment, volume estimates, and the errors introduced into both a static geomodel and dynamic reservoir simulation are discussed. This case study highlights the importance of optimizing well design for petrophysical data collection and demonstrates the potential for value creation. Extensive data collection was initially carried out in both exploration and appraisal wells, including full sets of logging while drilling (LWD), wireline logging, fluid sampling, and extensive coring. Both wells were drilled with considerable overbalanced mud weights due to the risk of overpressured reservoirs in the region. The log data was subsequently corrected for significant mud-filtration invasion, with calibration to core measurements guiding the interpretation. Geological and reservoir models were built based on results from the two wells, and development wells were planned accordingly. A thorough investigation of core material raised suspicion that there could also be a significant adverse effect of core properties resulting from overbalanced drilling. The implications were so significant for the reservoir volume that a strategic decision was made to drill a geopilot well close to the initial exploration well, prior to field development drilling. The well was drilled six years after the initial exploration phase with considerably lower overbalance. Extensive well data, including one core, were acquired. The recovered core was crucial in order to compare the reservoir properties for comparable facies between all three wells. The results from the core demonstrate distinctly different rock quality characteristics, especially at the high end of the reservoir quality spectrum. Results of the core study confirmed the initial hypothesis that overbalanced drilling had significantly impacted the properties of the core as well as the well logs. The study concluded that the updated reservoir model properties would significantly increase the in-place volumes compared to the pre-geopilot estimate. This study shows how well design adversely affected petrophysical measurements and how errors in these data compromised geologica","PeriodicalId":153712,"journal":{"name":"SPWLA 62nd Annual Online Symposium Transactions","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134145138","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}
Pub Date : 2021-05-17DOI: 10.30632/SPWLA-2021-0082
M. Lefranc, Zikri Bayraktar, M. Kristensen, Hedi Driss, I. L. Nir, P. Marza, J. Kherroubi
Sedimentary geometry on borehole images usually summarizes the arrangement of bed boundaries, erosive surfaces, cross bedding, sedimentary dip, and/or deformed beds. The interpretation, very often manual, requires a good level of expertise, is time consuming, can suffer from user bias, and become very challenging when dealing with highly deviated wells. Bedform geometry interpretation from crossbed data is rarely completed from a borehole image. The purpose of this study is to develop an automated method to interpret sedimentary structures, including the bedform geometry, from borehole images. Automation is achieved in this unique interpretation methodology using deep learning. The first task comprised the creation of a training dataset of 2D borehole images. This library of images was then used to train machine learning (ML) models. Testing different architectures of convolutional neural networks (CNN) showed the ResNet architecture to give the best performance for the classification of the different sedimentary structures. The validation accuracy was very high, in the range of 93–96%. To test the developed method, additional logs of synthetic data were created as sequences of different sedimentary structures (i.e., classes) associated with different well deviations, with addition of gaps. The model was able to predict the proper class and highlight the transitions accurately.
{"title":"DEEP-LEARNING-BASED AUTOMATED SEDIMENTARY GEOMETRY CHARACTERIZATION FROM BOREHOLE IMAGES","authors":"M. Lefranc, Zikri Bayraktar, M. Kristensen, Hedi Driss, I. L. Nir, P. Marza, J. Kherroubi","doi":"10.30632/SPWLA-2021-0082","DOIUrl":"https://doi.org/10.30632/SPWLA-2021-0082","url":null,"abstract":"Sedimentary geometry on borehole images usually summarizes the arrangement of bed boundaries, erosive surfaces, cross bedding, sedimentary dip, and/or deformed beds. The interpretation, very often manual, requires a good level of expertise, is time consuming, can suffer from user bias, and become very challenging when dealing with highly deviated wells. Bedform geometry interpretation from crossbed data is rarely completed from a borehole image. The purpose of this study is to develop an automated method to interpret sedimentary structures, including the bedform geometry, from borehole images. Automation is achieved in this unique interpretation methodology using deep learning. The first task comprised the creation of a training dataset of 2D borehole images. This library of images was then used to train machine learning (ML) models. Testing different architectures of convolutional neural networks (CNN) showed the ResNet architecture to give the best performance for the classification of the different sedimentary structures. The validation accuracy was very high, in the range of 93–96%. To test the developed method, additional logs of synthetic data were created as sequences of different sedimentary structures (i.e., classes) associated with different well deviations, with addition of gaps. The model was able to predict the proper class and highlight the transitions accurately.","PeriodicalId":153712,"journal":{"name":"SPWLA 62nd Annual Online Symposium Transactions","volume":"507 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116033562","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}
Pub Date : 2021-05-17DOI: 10.30632/SPWLA-2021-0103
M. Sviridov, A. Mosin, Sergey Lebedev, Ron D. Thompson
While proactive geosteering, special inversion algorithms are used to process the readings of logging-while-drilling resistivity tools in real-time and provide oil field operators with formation models to make informed steering decisions. Currently, there is no industry standard for inversion deliverables and corresponding quality indicators because major tool vendors develop their own device-specific algorithms and use them internally. This paper presents the first implementation of vendor-neutral inversion approach applicable for any induction resistivity tool and enabling operators to standardize the efficiency of various geosteering services. The necessity of such universal inversion approach was inspired by the activity of LWD Deep Azimuthal Resistivity Services Standardization Workgroup initiated by SPWLA Resistivity Special Interest Group in 2016. Proposed inversion algorithm utilizes a 1D layer-cake formation model and is performed interval-by-interval. The following model parameters can be determined: horizontal and vertical resistivities of each layer, positions of layer boundaries, and formation dip. The inversion can support arbitrary deep azimuthal induction resistivity tool with coaxial, tilted, or orthogonal transmitting and receiving antennas. The inversion is purely data-driven; it works in automatic mode and provides fully unbiased results obtained from tool readings only. The algorithm is based on statistical reversible-jump Markov chain Monte Carlo method that does not require any predefined assumptions about the formation structure and enables searching of models explaining the data even if the number of layers in the model is unknown. To globalize search, the algorithm runs several Markov chains capable of exchanging their states between one another to move from the vicinity of local minimum to more perspective domain of model parameter space. While execution, the inversion keeps all models it is dealing with to estimate the resolution accuracy of formation parameters and generate several quality indicators. Eventually, these indicators are delivered together with recovered resistivity models to help operators with the evaluation of inversion results reliability. To ensure high performance of the inversion, a fast and accurate semi-analytical forward solver is employed to compute required responses of a tool with specific geometry and their derivatives with respect to any parameter of multi-layered model. Moreover, the reliance on the simultaneous evolution of multiple Markov chains makes the algorithm suitable for parallel execution that significantly decreases the computational time. Application of the proposed inversion is shown on a series of synthetic examples and field case studies such as navigating the well along the reservoir roof or near the oil-water-contact in oil sands. Inversion results for all scenarios confirm that the proposed algorithm can successfully evaluate formation model complexity, recover model para
{"title":"VENDOR-NEUTRAL STOCHASTIC INVERSION OF LWD DEEP AZIMUTHAL RESISTIVITY DATA AS A STEP TOWARD EFFICIENCY STANDARDIZATION OF GEOSTEERING SERVICES","authors":"M. Sviridov, A. Mosin, Sergey Lebedev, Ron D. Thompson","doi":"10.30632/SPWLA-2021-0103","DOIUrl":"https://doi.org/10.30632/SPWLA-2021-0103","url":null,"abstract":"While proactive geosteering, special inversion algorithms are used to process the readings of logging-while-drilling resistivity tools in real-time and provide oil field operators with formation models to make informed steering decisions. Currently, there is no industry standard for inversion deliverables and corresponding quality indicators because major tool vendors develop their own device-specific algorithms and use them internally. This paper presents the first implementation of vendor-neutral inversion approach applicable for any induction resistivity tool and enabling operators to standardize the efficiency of various geosteering services. The necessity of such universal inversion approach was inspired by the activity of LWD Deep Azimuthal Resistivity Services Standardization Workgroup initiated by SPWLA Resistivity Special Interest Group in 2016. Proposed inversion algorithm utilizes a 1D layer-cake formation model and is performed interval-by-interval. The following model parameters can be determined: horizontal and vertical resistivities of each layer, positions of layer boundaries, and formation dip. The inversion can support arbitrary deep azimuthal induction resistivity tool with coaxial, tilted, or orthogonal transmitting and receiving antennas. The inversion is purely data-driven; it works in automatic mode and provides fully unbiased results obtained from tool readings only. The algorithm is based on statistical reversible-jump Markov chain Monte Carlo method that does not require any predefined assumptions about the formation structure and enables searching of models explaining the data even if the number of layers in the model is unknown. To globalize search, the algorithm runs several Markov chains capable of exchanging their states between one another to move from the vicinity of local minimum to more perspective domain of model parameter space. While execution, the inversion keeps all models it is dealing with to estimate the resolution accuracy of formation parameters and generate several quality indicators. Eventually, these indicators are delivered together with recovered resistivity models to help operators with the evaluation of inversion results reliability. To ensure high performance of the inversion, a fast and accurate semi-analytical forward solver is employed to compute required responses of a tool with specific geometry and their derivatives with respect to any parameter of multi-layered model. Moreover, the reliance on the simultaneous evolution of multiple Markov chains makes the algorithm suitable for parallel execution that significantly decreases the computational time. Application of the proposed inversion is shown on a series of synthetic examples and field case studies such as navigating the well along the reservoir roof or near the oil-water-contact in oil sands. Inversion results for all scenarios confirm that the proposed algorithm can successfully evaluate formation model complexity, recover model para","PeriodicalId":153712,"journal":{"name":"SPWLA 62nd Annual Online Symposium Transactions","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131180760","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}
Pub Date : 2021-05-17DOI: 10.30632/SPWLA-2021-0113
Melanie A Jensen, Schlumberger, Lalitha Venkataramanan, Li Chen, S. Bose, P. Tilke, O. Mullins
The evaluation of downhole fluid analysis (DFA) measurements of asphaltene gradients provides the ability to determine the extent of asphaltene equilibrium and the operative reservoir fluid geodynamics (RFG) processes. Typically, equilibrium of reservoir fluids indicates reservoir connectivity, a primary concern in field development planning. Currently, the modeling of asphaltene gradients is done through the manual evaluation of the DFA optical density gradients. The optical density measurements are fit to an equation of state (EOS), such as the Flory-Huggins-Zuo EOS, and evidence for asphaltene equilibrium is concluded if the inferred asphaltene diameter corresponds to that of the Yen-Mullins model for asphaltene composition. In this work, we present an automated Bayesian algorithm that proposes multiple hypotheses for the state of asphaltene equilibrium. The proposed hypotheses honor DFA measurements; physical models for asphaltenes in equilibrium, such as the Yen-Mullins model; and prior domain knowledge of the reservoir, such as geological layers, faults, and flow units. The leading hypotheses are reported, and evidence for or against asphaltene equilibrium is concluded from inferred quantities. Our proposed method provides a faster way for domain experts to explore different reservoir realizations that honor the theory of asphaltenes gradients and previous knowledge about the reservoir. We verify our novel method on three case studies that are undergoing different RFG processes through comparison of the interpretation done by domain experts. While there are many reservoir complexities associated with each case study, we focus on whether the underlying RFG process corresponds to the asphaltenes in equilibrium. The first case study is a light oil reservoir in the Norwegian North Sea that is mostly in fluid equilibrium with exceptions at the flanks. The second case study is a black oil reservoir that has undergone a fault block migration after the reservoir fluids had a chance to achieve equilibrium. The last case study is a black oil reservoir in quasi-equilibrium due to biodegradation in the lower portion of the well.
{"title":"AUTOMATED WORKFLOW TO INDICATE RESERVOIR CONNECTIVITY THROUGH ASPHALTENE EQUILIBRIUM","authors":"Melanie A Jensen, Schlumberger, Lalitha Venkataramanan, Li Chen, S. Bose, P. Tilke, O. Mullins","doi":"10.30632/SPWLA-2021-0113","DOIUrl":"https://doi.org/10.30632/SPWLA-2021-0113","url":null,"abstract":"The evaluation of downhole fluid analysis (DFA) measurements of asphaltene gradients provides the ability to determine the extent of asphaltene equilibrium and the operative reservoir fluid geodynamics (RFG) processes. Typically, equilibrium of reservoir fluids indicates reservoir connectivity, a primary concern in field development planning. Currently, the modeling of asphaltene gradients is done through the manual evaluation of the DFA optical density gradients. The optical density measurements are fit to an equation of state (EOS), such as the Flory-Huggins-Zuo EOS, and evidence for asphaltene equilibrium is concluded if the inferred asphaltene diameter corresponds to that of the Yen-Mullins model for asphaltene composition. In this work, we present an automated Bayesian algorithm that proposes multiple hypotheses for the state of asphaltene equilibrium. The proposed hypotheses honor DFA measurements; physical models for asphaltenes in equilibrium, such as the Yen-Mullins model; and prior domain knowledge of the reservoir, such as geological layers, faults, and flow units. The leading hypotheses are reported, and evidence for or against asphaltene equilibrium is concluded from inferred quantities. Our proposed method provides a faster way for domain experts to explore different reservoir realizations that honor the theory of asphaltenes gradients and previous knowledge about the reservoir. We verify our novel method on three case studies that are undergoing different RFG processes through comparison of the interpretation done by domain experts. While there are many reservoir complexities associated with each case study, we focus on whether the underlying RFG process corresponds to the asphaltenes in equilibrium. The first case study is a light oil reservoir in the Norwegian North Sea that is mostly in fluid equilibrium with exceptions at the flanks. The second case study is a black oil reservoir that has undergone a fault block migration after the reservoir fluids had a chance to achieve equilibrium. The last case study is a black oil reservoir in quasi-equilibrium due to biodegradation in the lower portion of the well.","PeriodicalId":153712,"journal":{"name":"SPWLA 62nd Annual Online Symposium Transactions","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127515676","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}
Pub Date : 2021-05-17DOI: 10.30632/spwla-2021-0055
A. Kolomytsev, GazpromNeft, Y. Pronyaeva, Schlumberger
Most conventional log interpretation technics use the radial model, which was developed for vertical wells and work well in them. But applying this model to horizontal wells can result in false conclusions. The reasons for this are property changes in vertical direction and different depth of investigation (DOI) of logging tools. DOI area probably can include a response from different layers with different properties. All of this complicates petrophysical modeling. The 3D approach for high angle well evaluation (HAWE) is forward modeling in 3D. For this modeling, it is necessary to identify the geological concept near the horizontal well section using multiscale data. The accuracy of modeling depends on the details of the accepted geological model based on the data of borehole images, logs, geosteering inversion, and seismic data. 3D modeling can be applied to improve the accuracy of reservoir characterization, well placement, and completion. The radial model is often useless for HAWE because LWD tools have different DOI and the invasion zone was not formed. But the difference between volumetric and azimuthal measurements is important for comprehensive interpretation because various formations have different properties in vertical directions. Resistivity tools have the biggest DOI. It is important to understand and be able to determine the reason for changes in log response: a change in the properties of the current layer or approaching the layers with other properties. For this, it is necessary to know the distance to the boundaries of formations with various properties and, therefore, to understand the geological structure of the discovered deposits, and such information on the scale of well logs can be obtained either by modeling or by using extra deep resistivity inversion (mapping). The largest amount of multidisciplinary information is needed for modeling purposes - from images and logs to mapping and seismic data. Case studies include successful examples from Western Siberia clastic formations. In frame of the cases, different tasks have been solved: developed geological concept, updated petrophysical properties for STOIIP and completion, and provided solutions during geosteering. Multiscale modeling, which includes seismic, geosteering mapping data, LWD, and imagers, has been used for all cases.
{"title":"3D PETROPHYSICS FOR HAWE: CASE STUDIES","authors":"A. Kolomytsev, GazpromNeft, Y. Pronyaeva, Schlumberger","doi":"10.30632/spwla-2021-0055","DOIUrl":"https://doi.org/10.30632/spwla-2021-0055","url":null,"abstract":"Most conventional log interpretation technics use the radial model, which was developed for vertical wells and work well in them. But applying this model to horizontal wells can result in false conclusions. The reasons for this are property changes in vertical direction and different depth of investigation (DOI) of logging tools. DOI area probably can include a response from different layers with different properties. All of this complicates petrophysical modeling. The 3D approach for high angle well evaluation (HAWE) is forward modeling in 3D. For this modeling, it is necessary to identify the geological concept near the horizontal well section using multiscale data. The accuracy of modeling depends on the details of the accepted geological model based on the data of borehole images, logs, geosteering inversion, and seismic data. 3D modeling can be applied to improve the accuracy of reservoir characterization, well placement, and completion. The radial model is often useless for HAWE because LWD tools have different DOI and the invasion zone was not formed. But the difference between volumetric and azimuthal measurements is important for comprehensive interpretation because various formations have different properties in vertical directions. Resistivity tools have the biggest DOI. It is important to understand and be able to determine the reason for changes in log response: a change in the properties of the current layer or approaching the layers with other properties. For this, it is necessary to know the distance to the boundaries of formations with various properties and, therefore, to understand the geological structure of the discovered deposits, and such information on the scale of well logs can be obtained either by modeling or by using extra deep resistivity inversion (mapping). The largest amount of multidisciplinary information is needed for modeling purposes - from images and logs to mapping and seismic data. Case studies include successful examples from Western Siberia clastic formations. In frame of the cases, different tasks have been solved: developed geological concept, updated petrophysical properties for STOIIP and completion, and provided solutions during geosteering. Multiscale modeling, which includes seismic, geosteering mapping data, LWD, and imagers, has been used for all cases.","PeriodicalId":153712,"journal":{"name":"SPWLA 62nd Annual Online Symposium Transactions","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127823401","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}
Pub Date : 2021-05-17DOI: 10.30632/SPWLA-2021-0117
Ruijia Wang, Jiajun Zhao, Taher A. Kortam
For conventional acoustic monopole sources in a logging-while-drilling (LWD) or wireline environment, shear slowness logs can be hard to obtain, particularly in slow formations where direct refracted shear-wave arrivals are often absent. For LWD dipole sources, formation flexural waves are often coupled with the lowest order of tool flexural waves, so the flexural mode does not approach shear wave slowness at low frequencies. A dispersion correction is required to extract shear slowness from LWD dipole data. Instead, a quadrupole firing, which generates screw waves, is considered the best LWD excitation mode for shear measurement. A fundamental feature of screw waves in an LWD environment is that their non-leaky cutoff frequency slowness is the formation shear slowness. However, slowness data near the cutoff frequency of LWD screw waves are often influenced by noise or the presence of other modes because of low excitation amplitude. To overcome these LWD data processing challenges, we propose a data-driven processing method that uses all useful dispersion responses of existing modes in the frequency domain. The process first generates a differential phase frequency-slowness coherence map and extracts the slowness dispersion vs. frequency. Then, it computes the slowness density log, referring to the intensity of the dispersion response along the slowness axis. Next, an edge-detection method is applied to capture the edge of the first peak associated with shear slowness on the slowness density map. To refine the shear slowness answer, this initial estimate of shear slowness serves as the input to another algorithm that minimizes the misfit between the screw slowness vector and a simplified screw dispersion model. The simplified screw dispersion model consists of a pre-computed base library of theoretical screw dispersion curves and two data-driven parameters. The two data-driven parameters are used by the measured data to stretch the base dispersion model in the frequency and slowness axes, respectively, to account for errors generated by alteration, anisotropy, or other parameters not included in the forward modeling. The method can also be applied to flexural waves, where the initial guess of shear slowness is picked from the slowness density map of flexural waves after dispersion-correction processing. This paper shows a case study of borehole flexural and screw waves processing in soft formations. A modified differential-phase frequency-semblance (MDPFS) approach is applied to extract the mode waves' full-frequency dispersion response from measured waveforms. The data-driven shear slowness processing is applied to the dispersion response. Both dipole flexural waves and quadrupole screw waves are processed. A combination of slowness density log from the flexural or screw wave slowness and the dispersion-corrected slowness is used as a QC metric of the final estimated shear. Results show that flexural and screw dispersions are well measured by th
{"title":"APPLICATION OF LWD ACOUSTIC DISPERSIVE DATA PROCESSING FOR HIGH-QUALITY SHEAR SLOWNESS LOGS IN SLOW FORMATIONS","authors":"Ruijia Wang, Jiajun Zhao, Taher A. Kortam","doi":"10.30632/SPWLA-2021-0117","DOIUrl":"https://doi.org/10.30632/SPWLA-2021-0117","url":null,"abstract":"For conventional acoustic monopole sources in a logging-while-drilling (LWD) or wireline environment, shear slowness logs can be hard to obtain, particularly in slow formations where direct refracted shear-wave arrivals are often absent. For LWD dipole sources, formation flexural waves are often coupled with the lowest order of tool flexural waves, so the flexural mode does not approach shear wave slowness at low frequencies. A dispersion correction is required to extract shear slowness from LWD dipole data. Instead, a quadrupole firing, which generates screw waves, is considered the best LWD excitation mode for shear measurement. A fundamental feature of screw waves in an LWD environment is that their non-leaky cutoff frequency slowness is the formation shear slowness. However, slowness data near the cutoff frequency of LWD screw waves are often influenced by noise or the presence of other modes because of low excitation amplitude. To overcome these LWD data processing challenges, we propose a data-driven processing method that uses all useful dispersion responses of existing modes in the frequency domain. The process first generates a differential phase frequency-slowness coherence map and extracts the slowness dispersion vs. frequency. Then, it computes the slowness density log, referring to the intensity of the dispersion response along the slowness axis. Next, an edge-detection method is applied to capture the edge of the first peak associated with shear slowness on the slowness density map. To refine the shear slowness answer, this initial estimate of shear slowness serves as the input to another algorithm that minimizes the misfit between the screw slowness vector and a simplified screw dispersion model. The simplified screw dispersion model consists of a pre-computed base library of theoretical screw dispersion curves and two data-driven parameters. The two data-driven parameters are used by the measured data to stretch the base dispersion model in the frequency and slowness axes, respectively, to account for errors generated by alteration, anisotropy, or other parameters not included in the forward modeling. The method can also be applied to flexural waves, where the initial guess of shear slowness is picked from the slowness density map of flexural waves after dispersion-correction processing. This paper shows a case study of borehole flexural and screw waves processing in soft formations. A modified differential-phase frequency-semblance (MDPFS) approach is applied to extract the mode waves' full-frequency dispersion response from measured waveforms. The data-driven shear slowness processing is applied to the dispersion response. Both dipole flexural waves and quadrupole screw waves are processed. A combination of slowness density log from the flexural or screw wave slowness and the dispersion-corrected slowness is used as a QC metric of the final estimated shear. Results show that flexural and screw dispersions are well measured by th","PeriodicalId":153712,"journal":{"name":"SPWLA 62nd Annual Online Symposium Transactions","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134069031","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}
Pub Date : 2021-05-17DOI: 10.30632/SPWLA-2021-0032
Mohammed Alghazal, Dimitrios Krinis
Fluid saturation data obtained from core analysis are used as control points for log calibration, saturation modeling and sweep evaluation. These lab-derived data are often viewed as ground-truth values without fundamentally understanding the key limitations of experimental procedures or scrutinizing the accuracy of measured lab data. This paper presents a unique assessment of sponge core data through parameterization, uncertainty analysis and Monte-Carlo modeling of critical variables influencing lab-derived saturation results. This work examines typical lab data and reservoir information that could impact final saturation results in sponge coring. We dissected and analyzed ranges of standard raw data from Dean-Stark and spectrometric analysis (including, gravimetric weights, distilled water volumes, pore volumes and sponge’s absorbance), input variables of fluid and rock properties (such as, water salinity, formation volume factors, plug’s dimension and stress corrections), governing equations (including, salt correction factors, water density correlations and lab mass balance equations) and other factors (for instance, sources of water salinity, filtrate invasion, bleeding by gas liberation and water evaporation). Based on our investigation, we have identified and statistically parameterized 11 key variables to quantify the uncertainty in lab-derived fluid saturation data in sponge cores. The variables’ uncertainties were mapped into continuous distributions and randomly sampled by Monte-Carlo simulation to generate probabilistic saturation models for sponge cores. Simulation results indicate the significance of the water salinity parameter in mixed salinity environments, ranging between 20,000 to 150,000 ppm. This varied range of water salinity produces a wide uncertainty spectrum of core oil saturation in the range of +/- 3 to 10% saturation unit. Consequently, we developed two unique salinity variance models to capture the water salinity effect and minimize the uncertainty in the calculation of core saturation. The first model uses a material balance to solve for the salinity given the distilled water volume and gravimetric weight difference of the sample before and after leaching. The second model iteratively estimates the salinity required to achieve 100% of total fluids saturation at reservoir condition after correcting for the bleeding, stress and water evaporation effects. Our work shows that these derived models of water salinity are consistent with water salinity data from surface and bottom-hole samples. Despite the prominence of applications of core saturation data in many aspects of the industry, thorough investigation into its quality and accuracy is usually overlooked. To the best of our knowledge, this is the first paper to present a novel analysis of the uncertainty coupled with Monte-Carlo simulation of lab-derived saturation’s data from sponge cores. The modeling approach and results highlighted in this work provide the fund
{"title":"UNCERTAINTY QUANTIFICATION BY MONTE CARLO SIMULATION OF LAB-DERIVED SATURATION DATA FROM SPONGE CORES","authors":"Mohammed Alghazal, Dimitrios Krinis","doi":"10.30632/SPWLA-2021-0032","DOIUrl":"https://doi.org/10.30632/SPWLA-2021-0032","url":null,"abstract":"Fluid saturation data obtained from core analysis are used as control points for log calibration, saturation modeling and sweep evaluation. These lab-derived data are often viewed as ground-truth values without fundamentally understanding the key limitations of experimental procedures or scrutinizing the accuracy of measured lab data. This paper presents a unique assessment of sponge core data through parameterization, uncertainty analysis and Monte-Carlo modeling of critical variables influencing lab-derived saturation results. This work examines typical lab data and reservoir information that could impact final saturation results in sponge coring. We dissected and analyzed ranges of standard raw data from Dean-Stark and spectrometric analysis (including, gravimetric weights, distilled water volumes, pore volumes and sponge’s absorbance), input variables of fluid and rock properties (such as, water salinity, formation volume factors, plug’s dimension and stress corrections), governing equations (including, salt correction factors, water density correlations and lab mass balance equations) and other factors (for instance, sources of water salinity, filtrate invasion, bleeding by gas liberation and water evaporation). Based on our investigation, we have identified and statistically parameterized 11 key variables to quantify the uncertainty in lab-derived fluid saturation data in sponge cores. The variables’ uncertainties were mapped into continuous distributions and randomly sampled by Monte-Carlo simulation to generate probabilistic saturation models for sponge cores. Simulation results indicate the significance of the water salinity parameter in mixed salinity environments, ranging between 20,000 to 150,000 ppm. This varied range of water salinity produces a wide uncertainty spectrum of core oil saturation in the range of +/- 3 to 10% saturation unit. Consequently, we developed two unique salinity variance models to capture the water salinity effect and minimize the uncertainty in the calculation of core saturation. The first model uses a material balance to solve for the salinity given the distilled water volume and gravimetric weight difference of the sample before and after leaching. The second model iteratively estimates the salinity required to achieve 100% of total fluids saturation at reservoir condition after correcting for the bleeding, stress and water evaporation effects. Our work shows that these derived models of water salinity are consistent with water salinity data from surface and bottom-hole samples. Despite the prominence of applications of core saturation data in many aspects of the industry, thorough investigation into its quality and accuracy is usually overlooked. To the best of our knowledge, this is the first paper to present a novel analysis of the uncertainty coupled with Monte-Carlo simulation of lab-derived saturation’s data from sponge cores. The modeling approach and results highlighted in this work provide the fund","PeriodicalId":153712,"journal":{"name":"SPWLA 62nd Annual Online Symposium Transactions","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134583821","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}
Pub Date : 2021-05-17DOI: 10.30632/spwla-2021-0053
A. Jacques, Total Se, V. Jaffrezic, B. Brouard, S. Ahmed, A. Serry, Raymond Nguyen, Y. Bigno, Brouard Consulting, Adnoc Offshore
In current economic and environmental contexts, the optimization of long, horizontal well completion and the maximization of individual well performance are becoming increasingly important. The challenge is to be able to start improving the production efficiency while designing an adapted completion for each well without compromising the project economy. The cost-effective formation evaluation technique described in this paper allows rapid identification of dynamic heterogeneities along the reservoir after the drilling of a horizontal well. This key information then can be used to optimize well completion and treatment. This new approach, called WTLog, combines well testing and logging techniques and was introduced initially for the optimization of unconventional well completion (Jacques et al., 2019 and Manivannan et al. 2019). The log begins by circulating a low-viscosity liquid that can be injected in the formation through the mud cake. The brine circulation operation is run at the end of the drilling phase, after reaching TD of the drain while maintaining a constant wellhead pressure at the wellhead. The constant pressure control can be applied without a specific additional choke device when Managed Pressure Drilling (MPD) is used to drill the formation section. The inlet and outlet flowrates are measured accurately, and their difference corresponds to the apparent formation-injection rate. The depth of the interface between the two liquids inside the borehole is estimated from the flowrates and pressure measured at the wellhead. Combining these data allows derivation of the low-viscosity/liquid-injection profile along the open hole. A permeability log then can be derived by inversion. Well Test Logging has been applied successfully for the first time on two horizontal wells in a conventional carbonate reservoir. The interpretation results were benchmarked to static conventional openhole logs and validated against the data log obtained by the dynamic production log tool (PLT) performed after well start-up. This technique opens new perspectives for optimizing well completion in these carbonate-fractured formations for which porosity logs might not be a good permeability indicator and where conductive fractures seen on image logs are not always indicative of future production.
{"title":"WELL-TEST LOGGING TO ENDEAVOR MAPPING THE CARBONATES PERMEABILITY, OFFSHORE ABU DHABI","authors":"A. Jacques, Total Se, V. Jaffrezic, B. Brouard, S. Ahmed, A. Serry, Raymond Nguyen, Y. Bigno, Brouard Consulting, Adnoc Offshore","doi":"10.30632/spwla-2021-0053","DOIUrl":"https://doi.org/10.30632/spwla-2021-0053","url":null,"abstract":"In current economic and environmental contexts, the optimization of long, horizontal well completion and the maximization of individual well performance are becoming increasingly important. The challenge is to be able to start improving the production efficiency while designing an adapted completion for each well without compromising the project economy. The cost-effective formation evaluation technique described in this paper allows rapid identification of dynamic heterogeneities along the reservoir after the drilling of a horizontal well. This key information then can be used to optimize well completion and treatment. This new approach, called WTLog, combines well testing and logging techniques and was introduced initially for the optimization of unconventional well completion (Jacques et al., 2019 and Manivannan et al. 2019). The log begins by circulating a low-viscosity liquid that can be injected in the formation through the mud cake. The brine circulation operation is run at the end of the drilling phase, after reaching TD of the drain while maintaining a constant wellhead pressure at the wellhead. The constant pressure control can be applied without a specific additional choke device when Managed Pressure Drilling (MPD) is used to drill the formation section. The inlet and outlet flowrates are measured accurately, and their difference corresponds to the apparent formation-injection rate. The depth of the interface between the two liquids inside the borehole is estimated from the flowrates and pressure measured at the wellhead. Combining these data allows derivation of the low-viscosity/liquid-injection profile along the open hole. A permeability log then can be derived by inversion. Well Test Logging has been applied successfully for the first time on two horizontal wells in a conventional carbonate reservoir. The interpretation results were benchmarked to static conventional openhole logs and validated against the data log obtained by the dynamic production log tool (PLT) performed after well start-up. This technique opens new perspectives for optimizing well completion in these carbonate-fractured formations for which porosity logs might not be a good permeability indicator and where conductive fractures seen on image logs are not always indicative of future production.","PeriodicalId":153712,"journal":{"name":"SPWLA 62nd Annual Online Symposium Transactions","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129049915","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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