Genbao Shi

Geology-guided Quantification of Production-Forecast Uncertainty in Dynamic Model Inversion

The presence of a large number of geologic uncertainties and limited well data typically increase the challenges associated with hydrocarbon recovery forecasting. Although recent advances in geologic modeling enable the automation of the model generation process by means of next-generation geostatistical tools, the computation of the reservoir dynamic response with full-physics reservoir simulation remains a computationally expensive task, which in practice requires considering only a few (but which?) of the many probable realizations. This paper presents a workflow that demonstrates the potential of capturing the inherent model uncertainty more accurately and assists in production-forecast business decisions. This workflow uses a history matching approach that directly interfaces the Earth modeling software with a forward simulator. It relies on the rapid characterization of the main features of the geologic uncertainty space, represented by an ensemble of sufficiently diverse history matched model realizations at the high-resolution geological scale. This workflow generates a more accurate result by obeying known geostatistics (variograms) and well constraints. We implement a multi-step, Bayesian Markov chain Monte Carlo inversion in which the proxy model is guided by streamline-based sensitivities. This process eliminates the need to run a forward simulation for each model realization, which significantly reduces the computation time. Efficient model parameterization and updates in the wave-number domain, based on discrete cosine transform (DCT), is used for fast characterization of the main features of the geologic uncertainty space, including structural framework, stratigraphic layering, facies distribution, and petrophysical properties. The application of the history matching workflow is demonstrated with a case study the combines the geological model with approximately 900K cells, four different depositional environments, and 30 wells with a 10-year waterflood history. Finally, the method is described to dynamically rank the reconciled model realizations to identify the highest potential of capturing bypassed oil and to optimize business decisions for implementing improved oil recovery (IOR). The main features include the following:  Calculation of pattern-dissimilarity distances, which distinguish two individual model realization in terms of recovery response  Deployment of very fast streamlined simulations to evaluate distances  Application of pattern-recognition techniques to assign several realizations, representative for production forecasting, to full-physics simulation  Derivation of the probability distribution of dynamic model responses (e.g., recovery factors) from the intelligently selected simulation runs

Modeling Distribution of Geological Properties Using Local Continuity Directions

Summary We present an innovative technology for 3D volumetric modeling of geological properties, using a Maximum Continuity Field. The method provides the user unique opportunity to a) directly control the local continuity directions, b) interactively operate with “geologically intuitive” datasets and c) retain the maximum fidelity of geological model by postponing the creation of grid/mesh until the final stage of static model building. We validate the method by modeling a permeability distribution in a fluvial system of complex synthetic field-case.

A Method for Multi-Level Probabilistic History Matching and Production Forecasting: Application in a Major Middle East Carbonate Reservoir

We present a probabilistic, computer-assisted history-matching method that captures inherent model uncertainty, enhances the predictive value of reconciled models, and renders more accurate production forecasts that help reservoir characterization and ultimately improve oil recovery factor. The workflow interfaces between the geo-modeling application and reservoir simulator, preserves the geological detail by updating high-resolution models and identifies models with highest potential in oil-recovery. We successfully apply this automated workflow to history matching 50 years of oil production, 12 years of water injection and 8 years of forecasting in the pilot area in major, structurally complex Middle East carbonate reservoir.