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

Abstract

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

Multivariate Analysis and Data Mining of Well-Stimulation Data by Use of Classification-and-Regression Tree With Enhanced Interpretation and Prediction Capabilities

“…Various clustering techniques have found use in applications such as Web search, image retrieval, gene-expression analysis, recommendation systems, and market research based on transaction data (Jiang et al 2004;Daruru et al 2009). Recently, clustering techniques were also used in the petroleum industry for distance-based reservoir-uncertainty modeling (Scheidt and Caers 2009) and dynamic ranking of history-matched reservoir models to quantify uncertainty in prediction forecasting (Maučec et al 2011;Singh et al 2014). When the data sets are Year in (2007,2008,2009) Year in (2010,2011,2012) Year in (2007,2010,2011,2012) Year in (2008,2009) small or variables exhibit a low degree of variation, clustering does not add significant value.…”

Development of proxy models for petroleum reservoir simulation: a systematic literature review and state-of-the-art

“…Use of experimental design to develop response surface [32][33][34][35][36][37][38][39][40][41], integrated with Monte Carlo simulations to characterise the response surface and to estimate the uncertainty [42,43]. Application of Bayesian multi-stage MCMC approach, based on an approximation with a linear expansion to reduce high computational costs [44], more accurately obtained model uncertainty and also assists in productionforecast business decisions [45], with Bayesian workflow based on two-step MCMC inversion [46].…”

Next Generation Geological Modeling for Hydrocarbon Reservoir Characterization

“… Derivation of the forecast uncertainty from the outcome of these intelligently selected few full-physics simulations. Further details are available in (Scheidt and Caers, 2009;Maučec et al, 2011b).…”

“…11. Flowchart of the streamline-based, two-step MCMC algorithm as implemented in AHM workflow (Maučec et al 2007;Maučec et al 2011a;Maučec et al 2011b). …”

“…Selected results of QuantUM AHM workflow validation are given in Fig. 12, with additional information available in (Maučec et al 2011a;Maučec et al 2011b):  The behavior of (negative) entropy, S (Fig. 12a) and the objective function, defined as the logarithm of transition probability of two-step Metropolis-Hastings sampler (Fig.…”