Two geostatistical methods for history matching are presented. Both rely on the sequential simulation principle for generating geologically sound realizations. The first method relies on perturbing the sequential simulation through the perturbation of the conditional distribution models; the second method relies on the perturbation of random numbers. We show that both approaches are general in ...

Production forecasting is the basis for decision making in oil and gas industry, can be quite challenging, especially terms of complex geological modeling subsurface. To help solve this problem, assisted history matching built on ensemble-based analysis such as ensemble smoother Kalman filter useful estimating models that preserve realism have predictive capabilities. These methods tend, howeve...

      Many large oil and gas fields in the most productive world regions happen to be fractured. The exploration and development of these reservoirs is a true challenge for many operators. These difficulties are due to uncertainties in geological fracture properties such as aperture, length, connectivity and intensity distribution. To successfully address these challenges, it is paramount to im...

Reformulating the history matching problem from a least-square mathematical optimization into Markov Decision Process introduces method in which reinforcement learning can be utilized to solve problem. This provides mechanism where an artificial deep neural network agent interact with reservoir simulator and find multiple different solutions Such formulation allows for solving parallel by launc...

Reservoir modeling is a critical step in the planning and development of oil fields. Before a reservoir model can be accepted for forecasting future production, the model has to be updated with historical production data. This process is called history matching. History matching requires computer flow simulation, which is very time-consuming. As a result, only a small number of simulation runs ...

History matching is the process of modifying the uncertain attributes of a reservoir model to reproduce the real reservoir performance. It is a classical reservoir engineering problem and plays an important role in reservoir management since the resulting models are used to support decisions in other tasks such as economic analysis and production strategy. This work introduces a dynamic decisio...

W. H. CHEN G. R. GAVALAS J. H. SEINFELD M. L. WASSERMAN History-matching problems, in which reservoir parameters arc to be estimated from well pressure data, are formulated as optimal control problems. The necessary conditions for optimality lead naturally to gradient optimization methods for determining the optimal parameter estimates. The key feature of the approach is that reservoir properti...