Uncertainty Quantification in Reservoir Prediction: Part 1—Model Realism in History Matching Using Geological Prior Definitions

被引:0
作者
Dan Arnold
Vasily Demyanov
Temistocles Rojas
Mike Christie
机构
[1] Heriot-Watt University,Institute of Petroleum Engineering
[2] Seven Energy International,undefined
来源
Mathematical Geosciences | 2019年 / 51卷
关键词
Inverse problems; Uncertainty; Model calibration; Geostatistics; Reservoir modelling; Prior knowledge; Support vector; Classification; Natural analogues; Fluvial geology;
D O I
暂无
中图分类号
学科分类号
摘要
Bayesian uncertainty quantification of reservoir prediction is a significant area of ongoing research, with the major effort focussed on estimating the likelihood. However, the prior definition, which is equally as important in the Bayesian context and is related to the uncertainty in reservoir model description, has received less attention. This paper discusses methods for incorporating the prior definition into assisted history-matching workflows and demonstrates the impact of non-geologically plausible prior definitions on the posterior inference. This is the first of two papers to deal with the importance of an appropriate prior definition of the model parameter space, and it covers the key issue in updating the geological model—how to preserve geological realism in models that are produced by a geostatistical algorithm rather than manually by a geologist. To preserve realism, geologically consistent priors need to be included in the history-matching workflows, therefore the technical challenge lies in defining the space of all possibilities according to the current state of knowledge. This paper describes several workflows for Bayesian uncertainty quantification that build realistic prior descriptions of geological parameters for history matching using support vector regression and support vector classification. In the examples presented, it is used to build a prior description of channel dimensions, which is then used to history-match the parameters of both fluvial and deep-water reservoir geostatistical models. This paper also demonstrates how to handle modelling approaches where geological parameters and geostatistical reservoir model parameters are not the same, such as measured channel dimensions versus affinity parameter ranges of a multi-point statistics model. This can be solved using a multilayer perceptron technique to move from one parameter space to another and maintain realism. The overall workflow was implemented on three case studies, which refer to different depositional environments and geological modelling techniques, and demonstrated the ability to reduce the volume of parameter space, thereby increasing the history-matching efficiency and robustness of the quantified uncertainty.
引用
收藏
页码:209 / 240
页数:31
相关论文
共 104 条
  • [1] Abdollahzadeh A(2012)Bayesian optimisation algorithm applied to uncertainty quantification SPE J 17 865-873
  • [2] Reynolds A(2003)Lateral accretion packages (laps): an important reservoir element in deep water sinuous channels Mar Pet Geol 20 631-648
  • [3] Christie M(2010)The Orinoco turbidite system: tectonic controls on sea-floor morphology and sedimentation AAPG Bull 94 869-887
  • [4] Corne DW(2012)Method for stochastic inverse modeling of fault geometry and connectivity using flow data Math Geosci 44 147-168
  • [5] Davies B(1970)River channel patterns J Hydraul Div HY1 201-221
  • [6] Williams GJ(2006)Uncertainty quantification for porous media flows J Comput Phys 217 143-158
  • [7] Abreu V(2005)Error analysis and simulations of complex phenomena Los Alamos Sci 29 6-25
  • [8] Sullivan M(2006)Anatomy and growth pattern of Amazon deep-sea fan as revealed by long-range side-scan sonar (GLORIA) and high-resolution seismic studies AAPG Bull 72 885-911
  • [9] Pirmez C(2015)Calibration of channelized subsurface flow models using nested sampling and soft probabilities Adv Water Resour 75 14-30
  • [10] Mohrig D(2006)Width and thickness of fluvial channel bodies and valley fills in the geological record: a literature compilation and classification J Sediment Res 76 731-770