Abstract
The article proposes a way to develop a drilling rig operation mode classifier specialized to recognize pre-emergency situations appearable in commercial oil-and-gas well drilling. The classifier is based on the theory of image recognition and artificial neuronet taught on real geological and technological information obtained while drilling. To teach the neuronet, a modified backpropagation algorithm that can teach to reach the global extremum of a target function has been proposed. The target function was a relative recognition error to minimize in the teaching. Two approaches to form the drilling rig pre-emergency situation classifier based on a taught neuronet have been considered. The first one involves forming an output classifier of N different signals, each of which corresponds to a single recognizable situation and, and can be formed on the basis of the analysis of M indications, that is using a uniform indication vocabulary for all recognized situations. The second way implements a universal classifier comprising N specialized ones, each of which can recognize a single pre-emergency situation and having a single output.
Highlights
While drilling oil-and-gas wells, the drilling rig operator has to quickly solve many technological problems including an early detection of oil-and-gas-and-water showings and mud losses while drilling, drilling optimization depending on the geological tasks, technological operations recognition and timing, choosing and maintaining an efficient drilling mode, pre-emergency situation diagnostics in real time scale, and drilling equipment operation diagnostics [1]
The principal functions of the drilling rig state classifying stage are 1) real-time raw information processing and 2) classified target state determination based on its indications, using a special algorithm
The mathematical methods for solving problems in complex technical system diagnostics have been developed for several decades
Summary
While drilling oil-and-gas wells, the drilling rig operator has to quickly solve many technological problems including an early detection of oil-and-gas-and-water showings and mud losses while drilling, drilling optimization depending on the geological tasks, technological operations recognition and timing, choosing and maintaining an efficient drilling mode, pre-emergency situation diagnostics in real time scale, and drilling equipment operation diagnostics [1]. There exists an image recognition theory [2,3] that can process information as a set of parameters describing the recognized target, in order to conclude what is the class the recognized image (target) belongs to. The principal functions of the drilling rig state classifying stage are 1) real-time raw information processing and 2) classified target state determination based on its indications, using a special algorithm. The drilling rig state determination uses a ready classifier which should determine the dynamic target state already in the real time. Both effective and quick target state determination is crucial
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