SPSA for oil-parameter estimation and reservoir management

Oil Reservoir Properties Estimation Using Neural Networks

This paper investigates the applicability as wel{ as the accuracy of artificial neural networks for estimating specific parameters that describe reservoir properties based on seismic data, Our approach relies on JPL’s adjoint operators general purpose neural network code to determine the best suited architecture, We believe that results presented l; this work demonstrate that artificial neural ...

New Tools for Oil and Gas Reservoir Fluid Management

A Neural Stochastic Optimization Framework for Oil Parameter Estimation

The main objective of the present work is to propose and evaluate a neural stochastic optimization framework for reservoir parameter estimation, for which a history matching procedure is implemented by combining three independent sources of spatial and temporal information: production data, time-lapse seismic and sensor information. In order to efficiently perform large-scale parameter estimati...

A Multiresolution Approach to Reservoir Parameter Estimation Using Wavelet Analysis

This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum...

Oil Reservoir Production Forecasting with Uncertainty Estimation Using Genetic Algorithms

A genetic algorithm is applied to the problem of conditioning the petrophysical rock properties of a reservoir model on historic production data. This is a difficult optimization problem where each evaluation of the objective function implies a flow simulation of the whole reservoir. Due to the high computing cost of this function, it is imperative to make use of an efficient optimization metho...