Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the g...Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.展开更多
The basis of designing gasified drilling is to understand the behavior of gas/liquid two-phase flow in the wellbore. The equations of mass and momentum conservation and equation of fluid flow in porous media were used...The basis of designing gasified drilling is to understand the behavior of gas/liquid two-phase flow in the wellbore. The equations of mass and momentum conservation and equation of fluid flow in porous media were used to establish a dynamic model to predict wellbore pressure according to the study results of Ansari and Beggs-Brill on gas-liquid two-phase flow. The dynamic model was solved by the finite difference approach combined with the mechanistic steady state model. The mechanistic dynamic model was numerically implemented into a FORTRAN 90 computer program and could simulate the coupled flow of fluid in wellbore and reservoir. The dynamic model revealed the effects of wellhead back pressure and injection rate of gas/liquid on bottomhole pressure. The model was validated against full-scale experimental data, and its 5.0% of average relative error could satisfy the accuracy requirements in engineering design.展开更多
基金funded by the National Natural Science Foundation of China(General Program:No.52074314,No.U19B6003-05)National Key Research and Development Program of China(2019YFA0708303-05)。
文摘Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.
文摘The basis of designing gasified drilling is to understand the behavior of gas/liquid two-phase flow in the wellbore. The equations of mass and momentum conservation and equation of fluid flow in porous media were used to establish a dynamic model to predict wellbore pressure according to the study results of Ansari and Beggs-Brill on gas-liquid two-phase flow. The dynamic model was solved by the finite difference approach combined with the mechanistic steady state model. The mechanistic dynamic model was numerically implemented into a FORTRAN 90 computer program and could simulate the coupled flow of fluid in wellbore and reservoir. The dynamic model revealed the effects of wellhead back pressure and injection rate of gas/liquid on bottomhole pressure. The model was validated against full-scale experimental data, and its 5.0% of average relative error could satisfy the accuracy requirements in engineering design.
