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Prediction of Drilling Pipe Sticking by Active Learning Method (ALM)

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Prediction of Drilling Pipe Sticking by Active Learning Method (ALM) Vol. 4(7), pp. 173-183, November 2013 DOI: 10.5897/JPGE2013.0166 Journal of Petroleum and Gas ISSN 2I41-2677 ©2013 Academic Journals Engineering http://www.academicjournals.org/JPGE Full Length Research Paper Prediction of drilling pipe sticking by active learning method (ALM) Morteza Elahi Naraghi1*, Peyman Ezzatyar2, Saeid Jamshidi3 1Petroleum and Geosystems Engineering Department, University of Texas at Austin, Austin, TX. 2Science and Research Branch, Islamic Azad University, Tehran, Iran. 3Chemical and Petroleum Engineering Department, Sharif University of Technology, Tehran, Iran. Accepted 29 August, 2013 Stuck piping is a common problem with tremendous impact on drilling efficiency and costs in oil industry. Generally, the stuck pipe troubles are solved after their occurrences by using some standard techniques; here we attempt to predict the causes of occurrence of such problems to eschew risks and excessive drilling costs. If these risks are identified in advance, better solutions can be provided to reduce the associated consequences. Based on the literature, this problem is caused by numerous parameters, such as drilling fluid properties and the characteristics of the mud cake that is formed while drilling. In this study, an attempt is made to develop a model for stuck pipe prediction. To consider all aspects of pipe sticking and behavior of the involved variables, the fuzzy logic and active learning method (ALM) can be used as a primary predictive tool. Active Learning Method is a robust recursive fuzzy modeling without computational complexity. These methods are broadly used in many industries; including oil and gas. This paper proposes a systematic approach for pipe stuck prediction based on ALM. The results of this method are more accurate than other methods and prediction accuracy is close to perfect either in stuck or non-stuck cases. This study presents a case study in which the ALM is used successfully to estimate pipe sticking. Thus, the proposed method possesses reliable results for prediction of pipe stuck, and can be used in order to minimize the risk of pipe sticking. Key words: Pipe stuck prediction, active learning method (ALM), artificial intelligence, drilling engineering. INTRODUCTION Over several years oil industry is facing troubles downtime and remedial costs and well cost and time associated with the stuck pipes. Differential pipe sticking overruns as a non-productive time in terms of loss of rig is one of the stuck pipe mechanisms with a major impact days either due to stopping of drilling operations or an on drilling efficiency and well costs (Adams, 1977a; attempt to free the stuck pipe. This huge loss is always Weakley, 1990; Wisnie and Zheiwei, 1994). These accounted for in the well budget cost as a contingency occurrences are common everywhere in the world and factor for the risks associated with the stuck pipe are estimated to cost the industry hundreds of millions of problems in the well planning and drilling performance dollars annually. In some areas, events related to approach (Adams, 1977b; Beigler and Kuhn, 1994; differentially stuck pipe can be responsible for as much Wisnie and Zheiwei, 1994; Sharif, 1997; Aadnoy et al., as 40% of the total well cost. Differential pipe sticking 1999). The recent increase in drilling activity, shortage of problems generally result in the significant amount of experienced personnel and equipment, and drilling in *Corresponding author. E-mail: [email protected]. 174 J. Petroleum Gas Eng. higher-risks areas have increased the risk of stuck pipe either in the upward or downward direction. In a complete events in all drilling operations (Yarim et al., 2007). stuck pipe situation, neither circulation nor pipe The concept of differential pressure sticking of drill pipe movement are possible. was first reported by Helmick and Longley (1957) Although these symptoms are similar to Key Seat according to laboratory tests. They stated that pipe sticking, they usually occur under different drilling sticking results when the drill pipe becomes motionless conditions. Significant mud overbalance, as well as an against a permeable bed and a portion of the area of the exposed permeable section, must also exist for pipe is isolated by filter cake. Hempkins et al. (1987) differential sticking to occur. Clearly, as many reservoirs analyzed pipe sticking statistically based on drilling become depleted, a significant number of wells will be parameters. This was done by comparing the properties drilled with high overbalance pressures, thereby of non-pipe stuck wells with the ones that had stuck maintaining the industry’s concerns over differential piping. Then drilling operations were planned according sticking. to non-pipe stuck wells' characteristics. In that study, the The likelihood of differential sticking increases further parameters of 221 wells were investigated in 131 stuck with the length of the permeable section that is open to pipes' cases in Mexico's wells and the risk of stuck pipe the drilling fluid. The continued trend towards extended occurrence in others wells were estimated. Biegler and reach and horizontal drilling means that increasing Kuhn (1994) generated a data base including 22 drilling lengths of permeable formations are exposed. Clearly, parameters in 73 non-pipe stuck wells and 54 pipe the nature of the rock formations encountered certainly sticking wells in Mexico's gulf. Recently, some research is cannot be altered. Therefore, if those formations carry a being conducted in order to determine the characteristics high risk of differential sticking, this has to be accepted. of stuck pipe such as the depth of pipe sticking. Torne et Furthermore, high overbalance pressures may be al. (2011) tried to determine the depth of pipe sticking by unavoidable if they are needed to maintain well control or means of continuous Free-Pipe logs. These studies were wellbore stability in other parts of the open-hole section. the base of primary comparative analysis that could However, mud composition and properties can be identify the pipe sticking mechanisms in addition to its modified, within limits, in the prevention of differential probability prediction. Howard and Glover (1994) sticking. improved the prediction stuck pipes' models by applying In the past multivariate statistical analysis techniques statistical techniques in 100 wells of Mexico's gulf. These and simulated sticking testes using different drilling fluids models were used for prevention of pipe sticking and have been performed to identify and modify parameters operation saving. Siruvuri et al. (2006) recently presented that lead to differential pipe sticking in order to prevent or an application of Artificial Neural Network (ANN) methods minimize sticking. A review of published literature and for understanding the causes of differential stuck pipe. laboratory data establishes the importance of mud filter Miri et al. (2007) implemented ANN to predict the pipe cake properties (thickness, shear strength, and lubricity) sticking in Iranian offshore oil fields. Murillo et al. (2009) on the differential sticking tendencies of mud. did a study to predict and avoid pipe sticking based on Artificial Intelligence methods, such as Neural Networks adaptive fuzzy logic. Al-Baiyat and Heinze (2012) and Fuzzy logic, have the ability to represent complex investigated application of ANN and Support Vector stuck pipe situations, which involve several variables. Machines (SVM) in stuck pipe prediction. Jahanbakhshi The methodology enables drilling industry personnel to and Keshavarzi (2012) used SVM with Gaussian kernel estimate the risk of occurrences of stuck pipe not only function to predict differential pipe sticking. Elahi Naraghi during well planning but also during drilling. A proper et al (2013) did a comprehensive study to compare the prediction of the risk of differential pipe sticking will performance of different Neural Networks and Neuro identify the main causes of the problem and Fuzzy Systems in prediction of pipe stuck. consequently, the best techniques to prevent stuck pipe Differential pipe sticking occurs when a part of the drill can be done. string, casing, or logging tool becomes embedded in a This paper is organized as follows. First, an overview of mud solids filter cake and is held there by a significant active learning method is presented. The subsequent amount of differential pressure. This differential pressure methodology section discusses input and output is the pressure difference between the hydrostatic parameters, preprocessing step, and evaluation method pressure of mud and the formation pore pressure. are explained. Finally, the implementation results and Usually, because of the excessive differential pressure, accuracy of prediction are presented and analyzed in the the sticking takes place across porous and permeable numerical results part of this work. formations such as sandstone or limestone, where a mud filter cake builds up during drilling. It does not occur in very low permeability formations such as shale, where MATERIALS AND METHODS mud filter cakes normally do not form. Stuck pipe is As discussed above, in this paper a novel approach for prediction of identified as an impedance of drilling mud flow in the pipe sticking which is based on active learning method (ALM) is annular space and the difficulty of the pipe movement presented. In this method, our system possesses multiple inputs Naraghi et al. 175 and one output, which is the probability of pipe sticking. Our model planes. By implementing IDS method to constructed pattern image is trained by Active Learning Method. Next an overview of Active of each plane, two different types of information are elicited. The Learning Method is presented. first one is the narrow path and the other is the deviation of the spread data points around each narrow path. Figure 1 depicts a typical result of implementing IDS method to data by radius equal to Active learning method 1. This figure is a result of implementing ALM steps on data. This figure is a function between output and measured depth in the last Active Learning Method (Bagheri Shouraki and Honda, 1997) is a step of dividing.
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