Journal of Petroleum Science and Engineering, Apr 1, 2019
Abstract The lack of acoustic measurements places severe limitations on the application of well l... more Abstract The lack of acoustic measurements places severe limitations on the application of well log data to analyze rock physics. In such conditions, other petrophysical data can be used to predict the shear and compressional sonic travel time. This study presents a novel data-driven model based on a nonlinear autoregressive neural network with exogenous (NARX) input to estimate the shear and compressional sonic travel time due to its ability to accurately determine nonlinearity in sequential and temporal data. The architecture of the model comprises three-layers and ten hidden neurons with gamma ray log as exogenous input. The proposed NARX methodology is developed using 11 wells, six from the Norwegian continental shelf and five from West Africa. The results show that the wells provide sufficiently accurate predictions of the actual sonic well logs using the NARX model. The predicted sonic logs are used to estimate formation property parameters like sonic ratio, sonic difference, sonic porosity, and Poisson's ratio. This paper proves NARX is an affordable, efficient and accurate means to reproduce sonic well logs for formation evaluation.
Petrophysical and geomechanical properties of the formation such as Young’s modulus, bulk modulus... more Petrophysical and geomechanical properties of the formation such as Young’s modulus, bulk modulus, shear modulus, Poisson’s ratio, and porosity provide characteristic description of the hydrocarbon reservoir. It is well-established that static geomechanical properties are good representatives of reservoir formations; however, they are non-continuous along the wellbore, expensive and determining these properties may lead to formation damage. Dynamic geomechanical formation properties from acoustic measurements offer a continuous and non-destructive means to provide a characteristic description of the reservoir formation. In the absence of reliable acoustic measurements of the formation, such as sonic logs, the estimation of the dynamic geomechanical properties becomes challenging. Several techniques like empirical, analytical and intelligent systems have been used to approximate the property estimates. These techniques can also be used to approximate acoustic measurements thus enable...
Journal of Petroleum Exploration and Production Technology, 2020
Sonic well logs provide a cost-effective and efficient non-destructive tool for continuous dynami... more Sonic well logs provide a cost-effective and efficient non-destructive tool for continuous dynamic evaluation of reservoir formations. In the exploration and production of oil and gas reservoirs, these sonic logs contain crucial information about the formation. However, shear sonic logs are not acquired in all oil and gas exploration wells. More so, many offset wells are not run with the most recent sonic logging tools capable of measuring both shear and compressional sonic transit times due to the relatively high costs of running such equipment. And in wells where they are deployed, they are run only over limited intervals of the formation. Such wells lack continuous shear wave transit time measurements along the formation. In this study, an exponential Gaussian process model is presented. The model accurately predicts the shear wave transit times in the formations which lack reliable shear wave transit time measurements. The proposed model is developed using an array of well logs,...
Journal of Petroleum Science and Engineering, 2019
Abstract The lack of acoustic measurements places severe limitations on the application of well l... more Abstract The lack of acoustic measurements places severe limitations on the application of well log data to analyze rock physics. In such conditions, other petrophysical data can be used to predict the shear and compressional sonic travel time. This study presents a novel data-driven model based on a nonlinear autoregressive neural network with exogenous (NARX) input to estimate the shear and compressional sonic travel time due to its ability to accurately determine nonlinearity in sequential and temporal data. The architecture of the model comprises three-layers and ten hidden neurons with gamma ray log as exogenous input. The proposed NARX methodology is developed using 11 wells, six from the Norwegian continental shelf and five from West Africa. The results show that the wells provide sufficiently accurate predictions of the actual sonic well logs using the NARX model. The predicted sonic logs are used to estimate formation property parameters like sonic ratio, sonic difference, sonic porosity, and Poisson's ratio. This paper proves NARX is an affordable, efficient and accurate means to reproduce sonic well logs for formation evaluation.
Journal of Petroleum Science and Engineering, Apr 1, 2019
Abstract The lack of acoustic measurements places severe limitations on the application of well l... more Abstract The lack of acoustic measurements places severe limitations on the application of well log data to analyze rock physics. In such conditions, other petrophysical data can be used to predict the shear and compressional sonic travel time. This study presents a novel data-driven model based on a nonlinear autoregressive neural network with exogenous (NARX) input to estimate the shear and compressional sonic travel time due to its ability to accurately determine nonlinearity in sequential and temporal data. The architecture of the model comprises three-layers and ten hidden neurons with gamma ray log as exogenous input. The proposed NARX methodology is developed using 11 wells, six from the Norwegian continental shelf and five from West Africa. The results show that the wells provide sufficiently accurate predictions of the actual sonic well logs using the NARX model. The predicted sonic logs are used to estimate formation property parameters like sonic ratio, sonic difference, sonic porosity, and Poisson's ratio. This paper proves NARX is an affordable, efficient and accurate means to reproduce sonic well logs for formation evaluation.
Petrophysical and geomechanical properties of the formation such as Young’s modulus, bulk modulus... more Petrophysical and geomechanical properties of the formation such as Young’s modulus, bulk modulus, shear modulus, Poisson’s ratio, and porosity provide characteristic description of the hydrocarbon reservoir. It is well-established that static geomechanical properties are good representatives of reservoir formations; however, they are non-continuous along the wellbore, expensive and determining these properties may lead to formation damage. Dynamic geomechanical formation properties from acoustic measurements offer a continuous and non-destructive means to provide a characteristic description of the reservoir formation. In the absence of reliable acoustic measurements of the formation, such as sonic logs, the estimation of the dynamic geomechanical properties becomes challenging. Several techniques like empirical, analytical and intelligent systems have been used to approximate the property estimates. These techniques can also be used to approximate acoustic measurements thus enable...
Journal of Petroleum Exploration and Production Technology, 2020
Sonic well logs provide a cost-effective and efficient non-destructive tool for continuous dynami... more Sonic well logs provide a cost-effective and efficient non-destructive tool for continuous dynamic evaluation of reservoir formations. In the exploration and production of oil and gas reservoirs, these sonic logs contain crucial information about the formation. However, shear sonic logs are not acquired in all oil and gas exploration wells. More so, many offset wells are not run with the most recent sonic logging tools capable of measuring both shear and compressional sonic transit times due to the relatively high costs of running such equipment. And in wells where they are deployed, they are run only over limited intervals of the formation. Such wells lack continuous shear wave transit time measurements along the formation. In this study, an exponential Gaussian process model is presented. The model accurately predicts the shear wave transit times in the formations which lack reliable shear wave transit time measurements. The proposed model is developed using an array of well logs,...
Journal of Petroleum Science and Engineering, 2019
Abstract The lack of acoustic measurements places severe limitations on the application of well l... more Abstract The lack of acoustic measurements places severe limitations on the application of well log data to analyze rock physics. In such conditions, other petrophysical data can be used to predict the shear and compressional sonic travel time. This study presents a novel data-driven model based on a nonlinear autoregressive neural network with exogenous (NARX) input to estimate the shear and compressional sonic travel time due to its ability to accurately determine nonlinearity in sequential and temporal data. The architecture of the model comprises three-layers and ten hidden neurons with gamma ray log as exogenous input. The proposed NARX methodology is developed using 11 wells, six from the Norwegian continental shelf and five from West Africa. The results show that the wells provide sufficiently accurate predictions of the actual sonic well logs using the NARX model. The predicted sonic logs are used to estimate formation property parameters like sonic ratio, sonic difference, sonic porosity, and Poisson's ratio. This paper proves NARX is an affordable, efficient and accurate means to reproduce sonic well logs for formation evaluation.
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