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Automatic drilling method based on deep learning

A deep learning and drilling technology, which is applied to the automatic control system of drilling, drilling equipment, earthwork drilling and production, etc., can solve the problems of increasing the labor intensity of engineers, and achieve the effect of reasonable planning of control methods

Active Publication Date: 2019-11-26
CHINA NAT PETROLEUM CORP CHUANQING DRILLING ENG CO LTD +1
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Problems solved by technology

Moreover, repeated wellbore trajectory adjustments will increase the labor intensity of engineers

Method used

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  • Automatic drilling method based on deep learning

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Embodiment Construction

[0018] Hereinafter, the automatic drilling method based on deep learning of the present invention will be described in detail with reference to the accompanying drawings and exemplary embodiments.

[0019] In order to realize the intelligence and automation of drilling, and to solve the problems of well trajectory planning, drilling mode control wellbore control and drilling parameters, the present invention can form a certain block by deep learning a large number of different drilling tool combinations corresponding to the drilling efficiency data Drilling model of the operating well, and planning the operating mode and operating parameters of the operating well section according to the trajectory design requirements of the operating well. During the actual drilling process, the present invention can optimize the trajectory control node and its drilling parameters in real time by continuously evaluating the drilling efficiency of each drilling mode switching node. Among them, th...

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Abstract

The invention provides an automatic drilling method based on deep learning. The method can comprise the following steps of building a drilling database; forming a drilling model on the basis of the drilling database; inputting basic information of a well to be operated, designed well hole track data and drilling tool combinations into a drilling tool model, wherein the drilling tool model can simulate the drilling efficiency and output a drilling scheme; performing drilling operation on the basis of the drilling scheme; and measuring the well hole track data in real time. The drilling model performs real-time evaluation on the basis of the measured data. Under the condition that the evaluation is not qualified, the drilling model plans the drilling scheme again, and the drilling operationis continuously performed according to the planned drilling scheme. Compared with the prior art, the automatic drilling method based on deep learning has the beneficial effects that the goal of intelligently controlling the well hole track and the drilling efficiency can be achieved; and engineers can be liberated from high-intensity brainwork and work through the reasonable plan on a control mode.

Description

Technical field [0001] The present invention relates to the technical field of petroleum drilling, in particular, to an automatic drilling method based on deep learning. Background technique [0002] In the process of oil and gas development, in order to extract oil and gas resources with the minimum number of drilling holes, or to reduce the impact of ground and underground conditions on oil and gas development, oil and gas workers invented the non-vertical well drilling technology in the 1930s. The expected offset is drilled in the formation to form a directional well or a horizontal well. [0003] At present, there are two main technologies for realizing the drilling of directional wells and horizontal wells in the formation as expected: One is rotary steering drilling technology, which generates the expected force or rotation angle at the position of the drill bit through the downhole driving device under the rotating condition to realize the drilling hole Offset in the expect...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): E21B44/00G06F17/50
CPCE21B44/00
Inventor 刘伟汪洋张德军陈东韩烈祥连太炜谢意高林戢利衡谭清明肖占朋
Owner CHINA NAT PETROLEUM CORP CHUANQING DRILLING ENG CO LTD
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