基于反距离加权插值法的产量劈分新方法
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摘要
多层油藏开发实践表明,准确的产量劈分是实施油田开发增产措施和提高最终采收率的关键。常规的产量劈分方法在多层油藏产量劈分实际应用中存在诸多局限性,已难以解决多层油藏产量劈分问题。为更好地提高多层油藏产量劈分结果的精度,在常规产量劈分方法分析的基础上,基于反距离加权插值法,建立一种新的产量劈分预测模型,并运用遗传学算法,将不确定的因素整体优化求解,进而进行多层油藏产量劈分。该方法应用于M油田X区块产量劈分,与常规方法相比,其计算结果较实测值偏差7.4%,与实际情况吻合度高,优势明显,能够较好解决多层油藏产量劈分问题,为实际油藏剩余油挖潜、提高采收率提供了重要数据。
The meticulous development of multi-layer reservoirs practice shows that accurate production cleavage is the key to implementing oilfield development stimulation measures and increasing ultimate recovery ratio. The conventional production cleavage method has many limitations in practical application of production cleavage for multi-layer reservoirs, and it is difficult to solve the problem. In order to better improve the accuracy of the cleavage results of multi-layer reservoir, based on the analysis of conventional method to cleave production, using mathematical processing by inverse distance weighted interpolation, a new prediction model of production cleavage is established, and Genetic Algorithm(GA) is used to achieve the changing and uncertain factors in the overall optimization solution. The new method is applied to the production splitting of X Block of a certain M reservoir in the middle oilfield. Compared with the conventional method, the calculation result deviation is within 7.4%, which shows better agreement with the actual situation and obvious advantage. This method can better solve the multi-layer reservoir problem of production cleavage and provides important data for tapping remaining oil potential in actual reservoirs and improve oil recovery.
The meticulous development of multi-layer reservoirs practice shows that accurate production cleavage is the key to implementing oilfield development stimulation measures and increasing ultimate recovery ratio. The conventional production cleavage method has many limitations in practical application of production cleavage for multi-layer reservoirs, and it is difficult to solve the problem. In order to better improve the accuracy of the cleavage results of multi-layer reservoir, based on the analysis of conventional method to cleave production, using mathematical processing by inverse distance weighted interpolation, a new prediction model of production cleavage is established, and Genetic Algorithm(GA) is used to achieve the changing and uncertain factors in the overall optimization solution. The new method is applied to the production splitting of X Block of a certain M reservoir in the middle oilfield. Compared with the conventional method, the calculation result deviation is within 7.4%, which shows better agreement with the actual situation and obvious advantage. This method can better solve the multi-layer reservoir problem of production cleavage and provides important data for tapping remaining oil potential in actual reservoirs and improve oil recovery.
引文
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[2]崔国峰,张红玲,王建国,等.多层油藏产量劈分新方法及应用[J].重庆科技学院学报(自然科学版),2014,16(1):81-84.
[3]杨欢,喻高明,鄢宇杰,等.产量劈分模式数值模拟研究[J].油气地球物理,2011,9(1):31-33.
[4]李坤,喻高明,王乔,等.一种新的产量劈分方法[J].油气地球物理,2011,9(4):19-22.
[5]李虹,许姗姗,史玉成.油水井产量劈分在多层油藏精细开发中的基础作用与应用[J].中国石油和化工标准与质量,2012,32(2):153-154.
[6]缪飞飞.动态预报方法在油田多峰产量预测中的应用[J].断块油气田,2018,25(3):358-362.
[7]张顺利,刘秀婷,王胜义,等.一种产量预测新方法的研究及探讨[J].断块油气田,2006,13(2):51-53.
[8]钟仪华,张志银,朱海双.特高含水期油田产量预测新方法[J].断块油气田,2011,18(5):641-644.
[9]曹光胜.厚层块状稠油油藏产量劈分方法研究[J].特种油气藏,2006,13(1):58-60.
[10]阚利岩,张建英,梁光迅,等.薄互层砂岩油藏产量劈分方法初探[J].特种油气藏,2002,9(增刊1):37-39.
[11]别爱芳,冀光,张向阳,等.产量构成法中措施产量劈分及预测的两种方法[J].石油勘探与开发,2007,34(5):628-632.
[12]李斌,张国旗,刘伟,等.年度措施产油量配产方法研究[J].石油学报,2001,22(2):70-78.
[13]靳国栋,刘衍聪,牛文杰.距离加权反比插值法和克里金插值法的比较[J].长春工业大学学报(自然科学版),2003,24(3):53-57.
[14]李新,程国栋,卢玲.空间内插方法比较[J].地球科学进展,2000,15(3):260-265.
[15] SEGOBYE M,胡友健.距离加权算法在大地水准面差距内插中的应用[J].工程地球物理学报,2006,3(6):443-447.
[16] YAO X. A review of evolutionary artificial neural networks[J].International Journal of Intelligent Systems,1993,8(4):539-567.
[17] HOLLAND J H. Adaptation in natural and artificial systems[J]. Ann Arbor,1975,6(2):126-137.
[18] SAMPSON J,BRINDLE A. Genetic algorithms for function optimization[D]. Edmonton:University of Alberta,1981.
[19] GOLDBERG D E. Real-coded genetic algorithm,virtual alphabets and blocking[J]. Complex Systems,1991,5(2):139-167.