The facies distribution of a reservoir is one of the biggest concerns for geologists,geophysicists,reservoir modelers,and reservoir engineers due to its high importance in the setting of any reliable decisionmaking/op...The facies distribution of a reservoir is one of the biggest concerns for geologists,geophysicists,reservoir modelers,and reservoir engineers due to its high importance in the setting of any reliable decisionmaking/optimization of field development planning.The approach for parameterizing the facies distribution as a random variable comes naturally through using the probability fields.Since the prior probability fields of facies come either from a seismic inversion or from other sources of geologic information,they are not conditioned to the data observed from the cores extracted from the wells.This paper presents a regularized element-free Galerkin(R-EFG)method for conditioning facies probability fields to facies observation.The conditioned probability fields respect all the conditions of the probability theory(i.e.all the values are between 0 and 1,and the sum of all fields is a uniform field of 1).This property achieves by an optimization procedure under equality and inequality constraints with the gradient projection method.The conditioned probability fields are further used as the input in the adaptive pluri-Gaussian simulation(APS)methodology and coupled with the ensemble smoother with multiple data assimilation(ES-MDA)for estimation and uncertainty quantification of the facies distribution.The history-matching of the facies models shows a good estimation and uncertainty quantification of facies distribution,a good data match and prediction capabilities.展开更多
In this work,we provide a more consistent alternative for performing value of information(VOI)analyses to address sequential decision problems in reservoir management and generate insights on the process of reservoir ...In this work,we provide a more consistent alternative for performing value of information(VOI)analyses to address sequential decision problems in reservoir management and generate insights on the process of reservoir decision-making.These sequential decision problems are often solved and modeled as stochastic dynamic programs,but once the state space becomes large and complex,traditional techniques,such as policy iteration and backward induction,quickly become computationally demanding and intractable.To resolve these issues and utilize fewer computational resources,we instead make use of a viable alternative called approximate dynamic programming(ADP),which is a powerful solution technique that can handle complex,large-scale problems and discover a near-optimal solution for intractable sequential decision making.We compare and test the performance of several machine learning techniques that lie within the domain of ADP to determine the optimal time for beginning a polymer flooding process within a reservoir development plan.The approximate dynamic approach utilized here takes into account both the effect of the information obtained before a decision is made and the effect of the information that might be obtained to support future decisions while significantly improving both the timing and the value of the decision,thereby leading to a significant increase in economic performance.展开更多
文摘The facies distribution of a reservoir is one of the biggest concerns for geologists,geophysicists,reservoir modelers,and reservoir engineers due to its high importance in the setting of any reliable decisionmaking/optimization of field development planning.The approach for parameterizing the facies distribution as a random variable comes naturally through using the probability fields.Since the prior probability fields of facies come either from a seismic inversion or from other sources of geologic information,they are not conditioned to the data observed from the cores extracted from the wells.This paper presents a regularized element-free Galerkin(R-EFG)method for conditioning facies probability fields to facies observation.The conditioned probability fields respect all the conditions of the probability theory(i.e.all the values are between 0 and 1,and the sum of all fields is a uniform field of 1).This property achieves by an optimization procedure under equality and inequality constraints with the gradient projection method.The conditioned probability fields are further used as the input in the adaptive pluri-Gaussian simulation(APS)methodology and coupled with the ensemble smoother with multiple data assimilation(ES-MDA)for estimation and uncertainty quantification of the facies distribution.The history-matching of the facies models shows a good estimation and uncertainty quantification of facies distribution,a good data match and prediction capabilities.
基金The authors acknowledge financial support from the Research Council of Norway through the Petromaks-2 project DIGIRES(RCN no.280473)the industrial partners AkerBP,Wintershall DEA,ENI,Petrobras,Equinor,Lundin,and Neptune Energy.
文摘In this work,we provide a more consistent alternative for performing value of information(VOI)analyses to address sequential decision problems in reservoir management and generate insights on the process of reservoir decision-making.These sequential decision problems are often solved and modeled as stochastic dynamic programs,but once the state space becomes large and complex,traditional techniques,such as policy iteration and backward induction,quickly become computationally demanding and intractable.To resolve these issues and utilize fewer computational resources,we instead make use of a viable alternative called approximate dynamic programming(ADP),which is a powerful solution technique that can handle complex,large-scale problems and discover a near-optimal solution for intractable sequential decision making.We compare and test the performance of several machine learning techniques that lie within the domain of ADP to determine the optimal time for beginning a polymer flooding process within a reservoir development plan.The approximate dynamic approach utilized here takes into account both the effect of the information obtained before a decision is made and the effect of the information that might be obtained to support future decisions while significantly improving both the timing and the value of the decision,thereby leading to a significant increase in economic performance.
