Reservoir Modeling Methods And Characterization Parameters For A Shoreface Reservoir: What Is Important For Fluid Flow Performance?

Abstract

About fifty reservoir characterizations were built using seven different modeling methods, and by varying a combination of reservoir characterization parameters using Experimental Design. The methods include:simple sequential gaussian simulation (SGS);SGS with stratification;SGS with reservoir quality map trend and no stratification;SGS with reservoir quality map trend and stratification;sequential screening simulation with 3D trend;truncated gaussian simulation with trend andgeneral marked point process. These fifty models were all subjected to fluid flow simulation with an active aquifer and limited peripheral waterflooding. The results show that oil recovery at 95% water cut ranges from 29% to 34% in these fifty models despite the large apparent visual differences between them. However, there are large differences, some greater than 200%, in water breakthrough time, water cut and cumulative water production. Based on fluid flow performance, these fifty models are classified into three groups. Group 1 includes models constructed using short variogram ranges in Methods 1 to 4, or medium variogram ranges without stratification in Methods 1 and 3. They behaved more homogeneously and produced flow characterized by later water breakthrough and lower watercut. Group 2 are those models constructed using long variogram ranges in Methods 1 to 4, or medium variogram range with stratification in Method 2 and 4. They behaved more heterogeneously and produced early water breakthrough and high overall watercuts. Even though the mean permeability was similar in the oil zone for the two groups of models, the effects of continuity imposed by the variogram and stratification controlled this disparate behavior. Models of Methods 5 to 7 comprise Group 3, which produced the earliest water breakthrough and the highest overall watercuts. We interpret the difference in water breakthrough time and watercut between Group 2 and 3 to be a result of the higher average permeability (by 100–200 mD) in the oil zone of Group 3 models. Do more complex, geologically realistic models provide the best frameworks for predicting flow performance in sandy shoreface reservoirs with good well control? Some of the simplest models we constructed provided similar flow simulation results as the most complex and soft-data conditioned models. What we do observe, however, is that the more soft conditioning data used and the more complex geologic models tend to give a restricted range in full field flow performance. Introduction Investment decisions for field development are commonly based on future field performance predicted from static and dynamic reservoir simulations. The investment decision relies on the predictive capability of the reservoir models. Challenges to reservoir geology are identifying what types of reservoir heterogeneities are most relevant to fluid flows so that the right types and amount of data can be acquired; and how to build robust reservoir models using limited subsurface information typically in a short time frame. Overall in the oil industry and as described below, there is a feeling that more soft conditioning data and more complex modeling procedures make reservoir characterization models that provide better frameworks to predict flow performance.

The application of outcrop analogues in geological modelling: a review, present status and future outlook

“…Van Wagoner & Bertram 1995) and more recently for the collection of sand-body geometry data (Arnot et al 1997;Jackson et al 2009;Sech et al 2009;Rittersbacher et al 2013 and numerous others). Outcrop analogues are used to provide information of facies belt thickness ) and the detail of intra-sand-body shales that potentially act as barriers (Jian et al 2004). Such shales may be modelled stochastically, with data on length and width drawn from outcrop data (Arnot et al 1997).…”