It is difficult to determine the safe operation window of drilling fluid density (SOWDFD) for deep igneous rock strata. Although the formation three-pressure (pore pressure, collapse pressure, and fracture pressure) prediction method with credibility improves the accuracy of formation three-pressure prediction, it still has a large error for deep igneous strata. To solve this problem, a modified method of the SOWDFD in deep igneous rock strata is proposed based on the leakage statistics of adjacent wells. This method is based on the establishment of the SOWDFD with credibility. Through statistical analysis of drilling fluid density of igneous rock leaky formation group in adjacent wells, the fracture leakage law of the formation is revealed and the upper limit of leak-off pressure containing probability information is obtained. Finally, the modified SOWDFD with credibility for deep igneous rock strata is formed. In this work, the proposed method was used to compute the SOWDFD with credibility of SHB well in Xinjiang, China. Results show that the modified density window is consistent with the field drilling conditions and can reflect the narrow density window in the Permian and lower igneous strata. Combined with the formation three-pressure prediction method with credibility and the actual leakage law of adjacent wells, it can effectively improve the prediction accuracy of the SOWDFD for deep igneous rock strata. The findings of the study can help in better understanding of the complex downhole geological environment in deep igneous rock strata and making reasonable drilling design scheme.
Shield is a typical mechanical, electrical, hydraulic integration of equipment. Its faults species are complex and diverse. To prevent because machine failure causes economic losses and casualties by shield, this article will introduces rough set theory to the subway shield machine fault diagnosis, propose a method which is based on rough set theory combined with neural network of Metro shield machine fault diagnosis. Use the strong advantage of rough sets theory in attribute reduction, and remove the data redundancy of information which is not effective for decisionmaking. Application of neural network algorithm to reduce date for diagnosis, the method can effectively improve the speed and accuracy of the diagnosis. Then use BP neural network combined with least square method to forecast fault, the least-square can reflect the trend of linear sequence, Neural network can seize the variation of nonlinear time series, therefore the combination of two methods could well predict the future of unit operating conditions.
For the deep strata with fracture zone, fault zone and various permeability and fracture loss, the geological conditions are particularly complicated, which makes it particularly difficult to predict the safety density window and engineering risk, through investigation and research, combined with the actual situation of a deep oil and gas reservoirs in northwest China, for the formation of three pressure section in the regions was determined, and through the actual leakage occurs correction of formation fracture pressure of drilling fluid density as a standard of lost circulation project risk evaluation, the author combined with big data, based on a certain algorithm, got the regionalization of the region narrow safe density window, on narrow density window combination of generalized stress and strength interference theory, based on the real-time drilling fluid density in the region one of the key Wells project risk evaluation, the project risk is match with the actual project risk situation
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