多点地质统计学中训练图像优选方法及其在地质建模中的应用
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摘要
在前人提出的高阶兼容性优选方法的基础上,提出一种基于数据事件重复概率的训练图像优选方法。其基本思路是提取条件数据中蕴含的数据事件,统计所提取的数据事件在训练图像中的重复次数并计算其重复概率,得到数据事件的无匹配率及其重复概率方差两个统计指标,用于表征训练图像中沉积模式的多样性与平稳性,评价其与建模区井数据蕴含的地质体空间结构的匹配性。无匹配率反映训练图像内地质模式的完备性,为首选指标;重复概率方差反映训练图像内地质模式的平稳性,为辅助指标,综合以上两种指标实现了对训练图像的优选。多组理论模型测试表明,重复概率方差小、无匹配率低的训练图像为最优训练图像。运用该方法对安哥拉Plutonio油田浊积水道训练图像进行优选,结果表明所建立的地质模型与地震属性吻合度高,能够更好地刻画水道的形态特征及砂体的分布模式。图9参24
Based on the analysis of the high-order compatibility optimization method proposed by predecessors, a new training image optimization method based on data event repetition probability is proposed. The basic idea is to extract the data event contained in the condition data and calculate the number of repetitions of the extracted data events and their repetition probability in the training image to obtain two statistical indicators, unmatched ratio and repeated probability variance of data events. The two statistical indicators are used to characterize the diversity and stability of the sedimentary model in the training image and evaluate the matching of the geological volume spatial structure contained in data of the well block to be modeled. The unmatched ratio reflects the completeness of geological model in training image, which is the first choice index. The repeated probability variance reflects the stationarity index of geological model of each training image, and is an auxiliary index. Then, we can integrate the above two indexes to achieve the optimization of training image. Multiple sets of theoretical model tests show that the training image with small variance and low no-matching ratio is the optimal training image. The method is used to optimize the training image of turbidite channel in Plutonio oilfield in Angola. The geological model established by this method is in good agreement with the seismic attributes and can better reproduce the morphological characteristics of the channels and distribution pattern of sands.
Based on the analysis of the high-order compatibility optimization method proposed by predecessors, a new training image optimization method based on data event repetition probability is proposed. The basic idea is to extract the data event contained in the condition data and calculate the number of repetitions of the extracted data events and their repetition probability in the training image to obtain two statistical indicators, unmatched ratio and repeated probability variance of data events. The two statistical indicators are used to characterize the diversity and stability of the sedimentary model in the training image and evaluate the matching of the geological volume spatial structure contained in data of the well block to be modeled. The unmatched ratio reflects the completeness of geological model in training image, which is the first choice index. The repeated probability variance reflects the stationarity index of geological model of each training image, and is an auxiliary index. Then, we can integrate the above two indexes to achieve the optimization of training image. Multiple sets of theoretical model tests show that the training image with small variance and low no-matching ratio is the optimal training image. The method is used to optimize the training image of turbidite channel in Plutonio oilfield in Angola. The geological model established by this method is in good agreement with the seismic attributes and can better reproduce the morphological characteristics of the channels and distribution pattern of sands.
引文
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[2]吴胜和,李文克.多点地质统计学:理论、应用与展望[J].古地理学报,2005,7(1):137-144.WU Shenghe,LI Wenke.Multiple-point geostatistics:Theory,application and perspective[J].Journal of Palaeogeography,2005,7(1):137-144.
[3]黄继新,穆龙新,陈和平,等.基于沉积界面的储集层建模方法初探[J].石油勘探与开发,2013,40(5):591-594.HUANG Jixin,MU Longxin,CHEN Heping,et al.Preliminary study on a depositional interface-based reservoir modeling method[J].Petroleum Exploration and Development,2013,40(5):591-594.
[4]尹艳树,张昌民,李少华,等.一种基于沉积模式的多点地质统计学建模方法[J].地质论评,2014,60(1):216-221.YIN Yanshu,ZHANG Changmin,LI Shaohua,et al.A pattern-based multiple point geostatistics method[J].Geological Review,2014,60(1):216-221.
[5]李阳,侯加根,李永强.碳酸盐岩缝洞型储集体特征及分类分级地质建模[J].石油勘探与开发,2016,43(4):600-606.LI Yang,HOU Jiagen,LI Yongqiang.Features and hierarchical modeling of carbonate fracture-cavity reservoirs[J].Petroleum Exploration and Development,2016,43(4):600-606.
[6]黄文松,王家华,陈和平,等.基于水平井资料进行地质建模的大数据误区分析与应对策略[J].石油勘探与开发,2017,44(6):939-947.HUANG Wensong,WANG Jiahua,CHEN Heping,et al.Big data paradox and modeling strategies in geological modeling based on horizontal wells data[J].Petroleum Exploration and Development,2017,44(6):939-947.
[7]ARPAT G B,CAERS J.Conditional simulation with patterns[J].Mathematical Geology,2007,39(2):177-203.
[8]BOISVERT J B,PYRCZ M J,DEUTSCH C V.Multiple-point statistics for training image selection[J].Natural Resources Research,2007,16(4):313-321.
[9]PéREZ C,MARIETHOZ G,ORTIZ J M.Verifying the high-order consistency of training images with data for multiple-point geostatistics[J].Computers&Geosciences,2014,70:190-205.
[10]李阳,吴胜和,侯加根,等.油气藏开发地质研究进展与展望[J].石油勘探与开发,2017,44(4):569-579.LI Yang,WU Shenghe,HOU Jiagen,et al.Progress and prospects of reservoir development geology[J].Petroleum Exploration and Development,2017,44(4):569-579.
[11]HALDORSEN H H,CHANG D M.Notes on stochastic shales;from outcrop to simulation model[C]//Reservoir Characterization.Orlando,Florida:Academic Press Incorporation,1986:445-485.
[12]LANTUéJOUL C.Geostatistical simulation.Models and algorithms[J].Minerva Ginecologica,2002,39(7/8):503-510.
[13]DEUTSCH C V,TRAN T T.Fluvsim:A program for object-based stochastic modeling of fluvial depositional systems[J].Computers&Geosciences,2002,28(4):525-535.
[14]PYRCZ M J,BOISVERT J B,DEUTSCH C V.Alluvsim:A program for event-based stochastic modeling of fluvial depositional systems[J].Computers&Geosciences,2009,35(8):1671-1685.
[15]张文彪,段太忠,刘彦锋,等.综合沉积正演与多点地质统计模拟碳酸盐岩台地:以巴西Jupiter油田为例[J].石油学报,2017,38(8):925-934.ZHANG Wenbiao,DUAN Taizhong,LIU Yanfeng,et al.Integrated sedimentary forward modeling and multipoint geostatistics in carbonate platform simulation:A case study of Jupiter oil field in Brazil[J].Acta Petrolei Sinica,2017,38(8):925-934.
[16]MICHAEL H A,LI H,BOUCHER A,et al.Combining geologic-process models and geostatistics for conditional simulation of 3D subsurface heterogeneity[J].Water Resources Research,2010,46(5):1532-1535.
[17]COMUNIAN A,JHA S K,GIAMBASTIANI B,et al.Training images from process-imitating methods[J].Mathematical Geosciences,2014,46(2):241-260.
[18]张文彪,段太忠,郑磊,等.基于浅层地震的三维训练图像获取及应用[J].石油与天然气地质,2015,36(6):1030-1037.ZHANG Wenbiao,DUAN Taizhong,ZHENG Lei,et al.Generation and application of three-dimensional MPS training images based on shallow seismic data[J].Oil&Gas Geology,2015,36(6):1030-1037.
[19]FADLELMULA F,KILLOUGH J M M,FRAIM M.TiConverter:Atraining image converting tool for multiple-point geostatistics[J].Computers&Geosciences,2016,96:47-55.
[20]ORTIZ J M,DEUTSCH C V.Indicator simulation accounting for multiple-point statistics[J].Mathematical Geology,2004,36(5):545-565.
[21]FENG W J,WU S,YIN Y,et al.A training image evaluation and selection method based on minimum data event distance for multiple-point geostatistics[J].Computers&Geosciences,2017,104:35-53.
[22]张文彪,段太忠,刘志强,等.深水浊积水道多点地质统计模拟:以安哥拉Plutonio油田为例[J].石油勘探与开发,2016,43(3):403-410.ZHANG Wenbiao,DUAN Taizhong,LIU Zhiqiang,et al.Application of multi-point geostatistics in deep-water turbidity channel simulation:A case study of Plutonio oilfield in Angola[J].Petroleum Exploration and Development,2016,43(3):403-410
[23]李少华,刘显太,王军,等.基于沉积过程建模算法Alluvsim的改进[J].石油学报,2013,34(1):140-144.LI Shaohua,LIU Xiantai,WANG Jun,et al.Improvement of the Alluvsim algorithm modeling based on depositional processes[J].Acta Petrolei Sinica,2013,34(1):140-144.
[24]STREBELLE S B,JOURNEL A G.Reservoir modeling using multiplepoint statistics[R].SPE 71324,2001.