基于数理统计方法的地质模型不确定性评价
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摘要
地质模型是进行油田开发指标预测的基础,油田开发初期,钻井资料少,用单一地质模型进行预测会带来很大的风险。采用不确定性评价方法,考虑地质建模参数的风险范围,综合正交试验设计和随机建模方法,建立多种方案的地质模型。运用响应曲面拟合和蒙特卡洛模拟等数理统计方法,通过概率计算对地质模型进行优选,选取反映油藏风险潜力的高中低模型。以优选的3个地质模型为基础,开展相同条件下的油藏数值模拟获得开发指标范围,从而实现油藏风险潜力评价。研究结果表明,该方法在资料较少、不确定性较大的油田开发初期,能够提供油田储量和开发指标的分布范围,对油田开发投资决策和科学管理具有重要意义。
Geological models are the basis for the prediction of oilfield development indicators. In the early stage of oilfield development, the prediction of using a single geological model would bring great risks due to few available drilling data. The geological model of various schemes is established by using the uncertainty evaluation method which considers the risk range of geological modeling parameters and applies orthogonal test design and stochastic modeling method. By using the method of response surface fitting and Monte Carlo simulation, the geological model is optimized by probability calculation, and the high and low models reflecting the reservoir risk potential are selected. Based on the three selected geological models, the reservoir numerical simulation under the same conditions is developed to achieve the range of development target, so as to realize the evaluation of reservoir risk potential. Research results show that the method can provide the range of oilfield reserves and development indicators when the data are fewerless and uncertainty is the larger in the early stage of field development. This method thus can help provide valuable information for the oil field development investment decision and scientific management.
Geological models are the basis for the prediction of oilfield development indicators. In the early stage of oilfield development, the prediction of using a single geological model would bring great risks due to few available drilling data. The geological model of various schemes is established by using the uncertainty evaluation method which considers the risk range of geological modeling parameters and applies orthogonal test design and stochastic modeling method. By using the method of response surface fitting and Monte Carlo simulation, the geological model is optimized by probability calculation, and the high and low models reflecting the reservoir risk potential are selected. Based on the three selected geological models, the reservoir numerical simulation under the same conditions is developed to achieve the range of development target, so as to realize the evaluation of reservoir risk potential. Research results show that the method can provide the range of oilfield reserves and development indicators when the data are fewerless and uncertainty is the larger in the early stage of field development. This method thus can help provide valuable information for the oil field development investment decision and scientific management.
引文
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[15] 徐伟,房磊,刘钧,等.浅层高渗砂岩油藏分级相控地质建模[J].地质科技情报,2017,37(1):197-201.
[16] 戴婉薇,范乐元,胡圣利,等.基于地质统计学反演和储层分类的相控储层建模方法研究及应用[J].地质科技情报,2017,36(1):236-241.
[17] 霍春亮,古莉,赵春明,等.基于地震、测井和地质综合一体化的储层精细建模[J].石油学报,2007,28(6):66-71.
[18] 赵继龙,王俊鹏,张先龙,等.基于地震多属性融合与地质统计学反演的薄层砂岩预测[J].地质科技情报,2014,33(5):96-99.
[19] 王根久,赵丽敏,李薇,等.随机建模中变差函数的敏感性研究[J].石油勘探与开发,2005,32(1):72-75.
[20] 崔永斌.有效储层物性下限值的确定方法[J].国外测井技术,2007,22(3):32-38.
[21] 崇仁杰,于兴河,李婷婷.应用实验设计优选随机建模的储层模型[J].石油与天然气地质,2012,33(1):94-100.
[22] 霍春亮,刘松,古莉,等.一种定量评价储集层地质模型不确定性的方法[J].石油勘探与开发,2007,34(5):574-579.
[23] 薛艳霞,廖新武,赵春明,等.基于随机建模技术的油田开发初期河流相储层不确定性分析方法[J].岩性油气藏,2012,24(1):80-83.
[24] 孔祥礼,卞炜.蒙特卡洛法在油田储量计算中的应用[J].断块油气田,2001,8(6):15-18.
[25] 高博禹,孙立春,胡光义,等.基于砂控地质建模和Monte Carlo模拟的储量评价方法[J].中国海上油气,2009,21(1):109-112.
[26] 陶磊,陶胤强.基于蒙特卡洛算法的能源供给量不确定性预测分析[J].重庆理工大学学报,2014,28(6):35-39.
[27] 张明泉,钟雄.蒙特卡洛模拟在油田开发经济评价风险中的应用[J].西南石油大学学报,2012,14(4):6-10.
[28] Al-Khalifah M,Makkawi M.The impact of data integration on geostatistical porosity modelling:A case study from the Berri Field,Saudi Arabia[J].Journal of Petroleum Geology,2002,25(4):485-498.
[29] Strebell E S.Conditional simulation of complex geological structures using multiple-point statistics[J].Mathematical Geology,2002,34(1):1-21.
[2] Gair R.Integrating deterministic and probabilistic reserves[C].SPE 82000,2003:1-7.
[3] Massonnat G J.Sampling space of uncertainty through stochastic modeling of geological facies [C].SPE 38746,1997:273-287.
[4] Butar F B,Iahiri P.On measures of uncertainty of empirical Bayes small-area estimators[J].Journal of Statistical Planning and Inference,2003,112(2):63-76.
[5] GAIRR.Integrating deterministic and probabilistic reserves[C].SPE 82000,2003.
[6] LUSCF.Probabilistic model to develop multilayer gas and oil prospects[C].SPE 69614,2001.
[7] Nobuo F,Hisaho N.Monte carlo algorithm for the double exchange model optimized for parallel computations[J].Computer Physics communications,2001,142 (3):410-412.
[8] Siddhartha C,Federico N,Neil S.Markov chain Monte Carlo methods for stochastic volatility models[J].Journal of Econometrics,2002,108(2):281-316.
[9] 吴胜和,杨延强.地下储层表征的不确定性及科学思维方法[J].地球科学与环境学报,2012,34(2):72-80.
[10] 周银邦,赵淑霞,张庆新,等.露头类比方法在地下储层建模中的应用进展[J].地质科技情报,2017,36(1):247-254.
[11] 孙立春,高博禹,李敬功.储层地质建模参数不确定性研究方法探讨[J].中国海上油气,2009,21(1):35-38.
[12] 肖红平,陈清华,李阳.随机建模在油气储量计算中的应用[J].新疆石油地质,2004, 25(3):317-319.
[13] 尹艳树,吴胜和,张昌民,等.用多种随机建模方法综合预测储层微相[J].石油学报,2006,27(2):68-71.
[14] 苏进昌,张岚,马新福.河流相储层开发初期地质建模[J].岩性油气藏,2008,20(3):114-118.
[15] 徐伟,房磊,刘钧,等.浅层高渗砂岩油藏分级相控地质建模[J].地质科技情报,2017,37(1):197-201.
[16] 戴婉薇,范乐元,胡圣利,等.基于地质统计学反演和储层分类的相控储层建模方法研究及应用[J].地质科技情报,2017,36(1):236-241.
[17] 霍春亮,古莉,赵春明,等.基于地震、测井和地质综合一体化的储层精细建模[J].石油学报,2007,28(6):66-71.
[18] 赵继龙,王俊鹏,张先龙,等.基于地震多属性融合与地质统计学反演的薄层砂岩预测[J].地质科技情报,2014,33(5):96-99.
[19] 王根久,赵丽敏,李薇,等.随机建模中变差函数的敏感性研究[J].石油勘探与开发,2005,32(1):72-75.
[20] 崔永斌.有效储层物性下限值的确定方法[J].国外测井技术,2007,22(3):32-38.
[21] 崇仁杰,于兴河,李婷婷.应用实验设计优选随机建模的储层模型[J].石油与天然气地质,2012,33(1):94-100.
[22] 霍春亮,刘松,古莉,等.一种定量评价储集层地质模型不确定性的方法[J].石油勘探与开发,2007,34(5):574-579.
[23] 薛艳霞,廖新武,赵春明,等.基于随机建模技术的油田开发初期河流相储层不确定性分析方法[J].岩性油气藏,2012,24(1):80-83.
[24] 孔祥礼,卞炜.蒙特卡洛法在油田储量计算中的应用[J].断块油气田,2001,8(6):15-18.
[25] 高博禹,孙立春,胡光义,等.基于砂控地质建模和Monte Carlo模拟的储量评价方法[J].中国海上油气,2009,21(1):109-112.
[26] 陶磊,陶胤强.基于蒙特卡洛算法的能源供给量不确定性预测分析[J].重庆理工大学学报,2014,28(6):35-39.
[27] 张明泉,钟雄.蒙特卡洛模拟在油田开发经济评价风险中的应用[J].西南石油大学学报,2012,14(4):6-10.
[28] Al-Khalifah M,Makkawi M.The impact of data integration on geostatistical porosity modelling:A case study from the Berri Field,Saudi Arabia[J].Journal of Petroleum Geology,2002,25(4):485-498.
[29] Strebell E S.Conditional simulation of complex geological structures using multiple-point statistics[J].Mathematical Geology,2002,34(1):1-21.
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