Decreasing the risks and geohazards associated with drilling engineering in high-temperature high-pressure(HTHP) geologic settings begins with the implementation of pre-drilling prediction techniques(PPTs). To improve...Decreasing the risks and geohazards associated with drilling engineering in high-temperature high-pressure(HTHP) geologic settings begins with the implementation of pre-drilling prediction techniques(PPTs). To improve the accuracy of geopressure prediction in HTHP hydrocarbon reservoirs offshore Hainan Island, we made a comprehensive summary of current PPTs to identify existing problems and challenges by analyzing the global distribution of HTHP hydrocarbon reservoirs, the research status of PPTs, and the geologic setting and its HTHP formation mechanism. Our research results indicate that the HTHP formation mechanism in the study area is caused by multiple factors, including rapid loading, diapir intrusions, hydrocarbon generation, and the thermal expansion of pore fluids. Due to this multi-factor interaction, a cloud of HTHP hydrocarbon reservoirs has developed in the Ying-Qiong Basin, but only traditional PPTs have been implemented, based on the assumption of conditions that do not conform to the actual geologic environment, e.g., Bellotti's law and Eaton's law. In this paper, we focus on these issues, identify some challenges and solutions, and call for further PPT research to address the drawbacks of previous works and meet the challenges associated with the deepwater technology gap. In this way, we hope to contribute to the improved accuracy of geopressure prediction prior to drilling and provide support for future HTHP drilling offshore Hainan Island.展开更多
Accurately predicting downhole risk before drilling in new exploration areas is one of the difficulties.Using intelligent algorithms to explore the complex relationship between multi-source data and downhole risk is a...Accurately predicting downhole risk before drilling in new exploration areas is one of the difficulties.Using intelligent algorithms to explore the complex relationship between multi-source data and downhole risk is a hot research topic and frontier in this field.However,due to the small number and uneven distribution of drilled wells in new exploration areas and the lack of sample data related to risk,the training model has insufficient generalization ability,and thus the prediction is not effective.In this paper,a drilling risk profile(depth domain)rich in geological and engineering information is constructed by introducing a quantitative evaluation method for drilling risk of drilled wells,which can provide sufficient risk sample data for model training and thus solve the small sample problem.For the problem of uneven distribution of drilling wells in new exploration areas,the concept of virtual wells and their deployment methods were proposed.Besides,two methods for calculating rock mechanical parameters of virtual wells were proposed,and the accuracy and applicability of the two methods are analyzed.The LSTM deep learning model was optimized to tap the quantitative relationship between drilling risk profiles and multi-source data(e.g.,seismic,logging,and rock mechanical parameters).The model was validated to have an average relative error of 9.19%.The quantitative prediction of the drilling risk profile of the virtual well was achieved using the trained LSTM model and the calculation of the relevant parameters of the virtual well.Finally,based on the sequential Gaussian simulation method and the risk distribution of drilled and virtual wells,a regional 3D drilling risk model was constructed.The analysis of real cases shows that the addition of virtual wells can significantly improve the identification of regional drilling risks and the prediction accuracy of pre-drill drilling risks in unexplored areas can be improved by up to 21%compared with the 3D risk model constructed based on drilled wells only.展开更多
基金funded by the National Basic Research Program of China (No. 2015CB251201)the NSFC-Shandong Joint Fund for Marine Science Research Centers (No. U1606401)+3 种基金the Scientific and Technological Innovation Project financially supported by Qingdao National Laboratory for Marine Science and Technology (No. 2016ASKJ13)the Major National Science and Technology Programs (No. 016ZX05024-001-002)the Natural Science Foundation of Hainan (No. ZDYF2016215)Key Science and Technology Foundation of Sanya (Nos. 2017PT13, 2017PT2014)
文摘Decreasing the risks and geohazards associated with drilling engineering in high-temperature high-pressure(HTHP) geologic settings begins with the implementation of pre-drilling prediction techniques(PPTs). To improve the accuracy of geopressure prediction in HTHP hydrocarbon reservoirs offshore Hainan Island, we made a comprehensive summary of current PPTs to identify existing problems and challenges by analyzing the global distribution of HTHP hydrocarbon reservoirs, the research status of PPTs, and the geologic setting and its HTHP formation mechanism. Our research results indicate that the HTHP formation mechanism in the study area is caused by multiple factors, including rapid loading, diapir intrusions, hydrocarbon generation, and the thermal expansion of pore fluids. Due to this multi-factor interaction, a cloud of HTHP hydrocarbon reservoirs has developed in the Ying-Qiong Basin, but only traditional PPTs have been implemented, based on the assumption of conditions that do not conform to the actual geologic environment, e.g., Bellotti's law and Eaton's law. In this paper, we focus on these issues, identify some challenges and solutions, and call for further PPT research to address the drawbacks of previous works and meet the challenges associated with the deepwater technology gap. In this way, we hope to contribute to the improved accuracy of geopressure prediction prior to drilling and provide support for future HTHP drilling offshore Hainan Island.
基金General Program of National Natural Science Foundation of China(52274024,52074326)。
文摘Accurately predicting downhole risk before drilling in new exploration areas is one of the difficulties.Using intelligent algorithms to explore the complex relationship between multi-source data and downhole risk is a hot research topic and frontier in this field.However,due to the small number and uneven distribution of drilled wells in new exploration areas and the lack of sample data related to risk,the training model has insufficient generalization ability,and thus the prediction is not effective.In this paper,a drilling risk profile(depth domain)rich in geological and engineering information is constructed by introducing a quantitative evaluation method for drilling risk of drilled wells,which can provide sufficient risk sample data for model training and thus solve the small sample problem.For the problem of uneven distribution of drilling wells in new exploration areas,the concept of virtual wells and their deployment methods were proposed.Besides,two methods for calculating rock mechanical parameters of virtual wells were proposed,and the accuracy and applicability of the two methods are analyzed.The LSTM deep learning model was optimized to tap the quantitative relationship between drilling risk profiles and multi-source data(e.g.,seismic,logging,and rock mechanical parameters).The model was validated to have an average relative error of 9.19%.The quantitative prediction of the drilling risk profile of the virtual well was achieved using the trained LSTM model and the calculation of the relevant parameters of the virtual well.Finally,based on the sequential Gaussian simulation method and the risk distribution of drilled and virtual wells,a regional 3D drilling risk model was constructed.The analysis of real cases shows that the addition of virtual wells can significantly improve the identification of regional drilling risks and the prediction accuracy of pre-drill drilling risks in unexplored areas can be improved by up to 21%compared with the 3D risk model constructed based on drilled wells only.
