Day 3 Thu, April 22, 2021最新文献

{"title":"Advanced Machine Learning Methods for Prediction of Fracture Closure Pressure","authors":"M. I. Mohamed, D. Mehta, Elsayed Abdelfatah, M. Ibrahim, E. Ozkan","doi":"10.2118/199974-ms","DOIUrl":"https://doi.org/10.2118/199974-ms","url":null,"abstract":"\u0000 Determining the closure pressure is crucial for optimal hydraulic fracturing design and successful execution of fracturing treatment. Historically, the use of diagnostic tests before the main fracturing treatment has significantly advanced to gain more information about the pattern of fracture propagation and fluid performance to optimize the designs. The goal is to inject a small volume of fracturing fluid to breakdown the formation and create small fracture geometry, then once pumping is stopped the pressure decline is analyzed to observe the fracture closure.\u0000 Many analytical methods such as G-Function, square root of time, etc. have been developed to determine the fracture closure pressure. There are cases in which there is difficulty in determining the fracture closure pressure, as well as personal bias and field experiences make it challenging to interpret the changes in the pressure derivative slope and identify fracture closure. These conditions include: High permeability reservoirs where fracture closure occurs very fast due to the quick fluid leakoff.Extremely low permeability reservoir, which requires a long shut-in time for the fluid to leak off and determine the fracture closure pressure.The non-ideal fluid leak-off behavior under complex conditions.\u0000 The objective of this study is to apply machine learning methods to implement a predesigned algorithm to execute the required tasks and predict the fracture closure pressure while minimizing the shortcomings in determining the closure pressure for non-ideal or subjective conditions.\u0000 This paper demonstrates training different supervised machine learning algorithms to help predict fracture closure pressure. The workflow involves using the datasets to train and optimize the models, which subsequently are used to predict the closure pressure of testing data. The output results are then compared with actual results from more than 120 DFIT data points. We further propose an integrated approach to feature selection and dataset processing and study the effects of data processing on the success of the model prediction.\u0000 The results from this study limit the subjectivity and the need for the experience of personal interpreting the data. We speculate that a linear regression and MLP neural network algorithms can yield high scores in the prediction of fracture closure pressure.","PeriodicalId":424988,"journal":{"name":"Day 3 Thu, April 22, 2021","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130821176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flow Profiling Using Fiber Optics in a Horizontal Steam Injector with Liner-Deployed Flow Control Devices","authors":"Mohammad Javaheri, M. Tran, R. Buell, T. Gorham, J. Munoz, J. Sims, Stephen Rivas","doi":"10.2118/200786-pa","DOIUrl":"https://doi.org/10.2118/200786-pa","url":null,"abstract":"\u0000 Horizontal steam injectors can improve the efficiency of thermal operations relative to vertical injectors. However, effective in-well and reservoir surveillance is needed to understand steam conformance. Uniform steam chest development improves steam-oil-ratio (SOR) in continuous steam injection and accelerates recovery in cyclic steam injection. Conformance of the injected steam can be achieved by flow control devices (FCD) deployed on either tubing or liner. A new liner-deployed FCD was used in a horizontal steam injector in the Kern River field. The liner-deployed FCD is intended to replace the tubing-deployed FCDs while reducing capital costs, surveillance costs, and well intervention costs for conformance control.\u0000 Fiber optics was used for surveillance, which is the most promising method in horizontal steam injectors considering reliability, accuracy, and cost. Fiber optic data enables monitoring the performance of liner-deployed FCDs as well as estimating the flow profile along the lateral length. Multi-mode Distributed Temperature Sensing (DTS) optical fibers and single-mode Distributed Acoustic Sensing (DAS) optical fibers were installed in the well for these objectives. Algorithms for interpreting DTS were improved to include a new technique, Shape Language Modeling (SLM), and a probabilistic approach. The configuration of the FCDs was changed during a well intervention, and it was monitored by DTS and DAS. Data from both DTS and DAS confirms the open/closed position of the sliding sleeve of FCDs initially and after the intervention. The probabilistic estimates of steam outflow in several FCD configurations match well with the theoretical outflow that is expected from the critical flow of steam through chokes installed in the FCDs.","PeriodicalId":424988,"journal":{"name":"Day 3 Thu, April 22, 2021","volume":"13 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114016601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}