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Dr. K. H. Coats



Reservoir Simulation Goals

Automatic Real-Time Probabilistic Global Predictive Optimization and Control



"Real-Time" and "and Control" are longer-term objectives.

We now have deterministic and probabilistic workflows with automated iterative optimization for all of these components, except for characterization in a geological model*.  The 4 iterative optimization problems are almost identical and differ only in the definition of the benefit function to be minimized or maximized, and identity of the adjusted variables.  Workflows for characterization, upscaling, and history matching are essentially the same.  They minimize uncertainty by minimizing a mismatch function.  Predictive optimization maximizes a value function such as BOE or NPV.

We and others** are working on integrating and automating the entire system in a continuous workflow (shown) that automatically evolves with time and new data to continuously generate better upscaled history matches, optimizations, and predictions and achieve our ultimate goals.  Automating the workflows and and the system requires batch processing of all components.  The probabilistic workflows require probabilistic representation and treatment of uncertain input data, from which probabilistic results can be robustly computed.  Many have referred to this automated system as the "Digital Oilfield", but most have so far limited their definitions and applications to surveillance and remedial response through direct or remote control, and as far as we know, no others have included a robust framework or method for treatment of uncertainty.

Probabilistic treatment is usually needed to answer questions in reservoir modeling, because our heterogeneous reservoirs are mostly unknown systems.  Reservoir properties are observed or interpreted only at sparse locations where wells are drilled, and in between wells those properties are populated in a geological model using geostatistical methods, which means that there are an infinite number of possible realizations that match the observed initial data, and that may equally well or sufficiently match history.  When there are large numbers of uncertainties, as there usually are, no individual realization (description) is ever correct, and only probabilistic results are meaningful.  The number of uncertain variables is generally equal to several times the number of gridblocks and wells, and may include boundary (operating) conditions that are a function of time.  See Uncertainty Quantification - P10, P50, P90, SensorPx, and SensorMatch.  We provide some basic probabilistic tools that use simple statistics and probability theory to form the building blocks of these probabilistic workflows.

Probabilistic forecasting is currently limited to small systems due to the large numbers of runs usually required to obtain a statistically significant set and reliable probabilistic results (or it requires large numbers of computers to make large numbers of simultaneous serial runs).  Optimizations however sometimes only require a few runs, and can often be performed using prototype models with no need for upscaling or history matching.

If the reservoir simulator could be replaced with some much simpler and faster model, such as a proxy model or a streamline model (or run simultaneously on very many computers), those probabilistic model size limitations could be practically eliminated.  Unfortunately, proxy methods become very difficult and expensive with very large numbers of variables, and others make assumptions that do not generally apply (eliminating them was the purpose for which numerical reservoir simulators were invented).  Solutions for single cell or 1D, or single component or single phase flow, or Buckley Leverett 2 phase immiscible flow, do not generally apply to multi-component multiphase flow in real reservoirs, and cannot be used to represent reservoir performance or to infer any characterization from performance (see What is Drainage Radius?).  Our probabilistic tools and workflows can handle an unlimited number of uncertain variables.


* Automated characterization requires a batch probabilistic geological model capable of producing unlimited numbers of equally-probable realizations of the uncertain, geostatistically-populated properties.  The characterization problem is equivalent to a history matching problem at time 0 on the geological model scale.

** Iterative optimization and workflow integration software is provided by third-parties.

2000 - 2022 Coats Engineering, Inc.