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The Rocks Abrasiveness Forecast Method Research

Posted on:2017-05-13Degree:MasterType:Thesis
Country:ChinaCandidate:H S XuFull Text:PDF
GTID:2271330488460500Subject:Oil-Gas Well Engineering
Abstract/Summary:PDF Full Text Request
Rock grinding is a tool to characterize rock wear. The results of the determination of rock grinding properties depend not only on the nature of the rock, but also on the drilling parameters. The existing method of in-situ measurement can only be obtained after the actual construction, and can only reflect the rock grinding under the specific drilling parameters, so it is not of general significance. The existing indoor test method, and the mechanism of the method of the determination of the grinding performance is different, resulting in the two of the grinding coefficient has a larger difference, can not be applied to the scene. Aiming at the problem o f the difference of the coefficient of ground grinding, this paper studied the prediction method of rock grinding in the field, which mainly includes:(1) Through the classification of wear form, combined with indoor and field rock grinding method for determination of, intercomparison of indoor and field grinding mechanism analysis, clarify the indoor scene abrasiveness index results in huge difference.(2) The study site drilling parameters in drilling pressure, speed, displacement and rock microstructure in quartz content, grain size of quartz and cementation degree of factors on rock abrasiveness mechanism and influence rules to determine rock grinding coefficient of various factors on the above.(3) Put forward the number of cuttings repeatedly crushing and the surface temperature of cutting area,and through the gray correlation analysis method, the influence factors of the rock grinding coefficient were analyzed, and the influence factors of the rock on the grinding performance were obtained.(4) Established with drilling parameters, and through the grinding chamber coefficient prediction on-site grinding coefficient of BP neural network prediction model, the indoor rock abrasiveness measurement results in the effective application of the prediction field abrasiveness index. Through calculation of an example, prediction of the rock abrasiveness value phase compared with the indoor predictive value, more close to the actual value, can meet the need for on-site drill grinding of predicted before construction, suitable for application in engineering field.
Keywords/Search Tags:rock abrasiveness, wear form, principal component analysis, neural network, prediction method
PDF Full Text Request
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