Adwait Chawathe

What is Relevant to Flow? A Comprehensive Study Using a Shallow Marine Reservoir

These days “estimating uncertainty” is the mantra. As we do this, we ask ourselves which is better: an array of geologically simple rapidly history-matched models, or a single geologically comprehensive, carefully history-matched model. After all, uncertainty, which is normally characterized by a range of forecasts from techniques such as Experimental Design, is difficult to quantify using just one model however comprehensive it may be. Yet if forecasts are obtained from a series of simple models, how good are they? Choosing one over the other also has significant implications in the time required for modeling, and also reservoir management. Specific questions, that directly affect the cost of modeling, come to mind. These are: What is the optimal level of geological detail, especially if the uncertainty management plan includes history-matching and simulating a series of models? Can the oil trapped behind the flood front be estimated by a series of simple lateral (shales) barriers/baffles or do we always need an extensive sequence stratigraphy framework? A detailed model may not be as amenable to uncertainty estimation by the virtue of its size. Is field-scale history matching adequate or is well-by-well history matching a must? Perhaps the brute force approach of probabilistic forward modeling provides the panacea. After all the proof-ofthe-pudding lies only in the model’s ability to accurately predict field performance. Finally, we also ask - should the modeling strategy, i.e., comprehensive vs. simple, be dependent on the response variable of interest, e.g. ultimate recovery factor vs. infill drill locations. As such, if ultimate recovery is the objective, a simple model may suffice. To answer questions like these, we re-visit current reservoir modeling paradigms. As a datum, we use a comprehensively modeled waterflood from Western Africa. This reservoir was modeled using extensive sequence stratigraphic techniques. The model was scaled-up from about 14 million cells to about 280,000 cells using a flow-based scale-up algorithm, carefully preserving all the mappable mudstones above flooding surfaces. History-matching for a 30-year period was systematically conducted with a team of field engineers and simulation specialists. The whole process took about a year to complete. Against this datum, we compare a series of rapidly built geological models that still honor all the data and the overall depositional architecture, and yet are significantly different from the datum geological model by the virtue of the modeling strategies implemented. The different modeling strategies vary in complexity from changes in variogram lengths and direction to simple tank models with stochastic sandstones and mudstones conditioned by well data. Various geostatistical algorithms were also investigated for facies modeling and petrophysical properties population within facies. The new models were history-matched using the conventional manual method and two separate assisted-history matching methods that use sensitivity coefficients. The question being addressed was: does constraining the geological models to the same dynamic data always create an imprint over the underlying geological variation and result in similar predictions? Preliminary results indicate that the history-matching overprint tends to mask some of the dramatic geological variations. This can have significant ramifications in modeling strategies, especially when assessing uncertainty in presence of substantial history.

Understanding Shale Gas Production Mechanisms Through Reservoir Simulation

Abstract Shale gas has changed the energy equation around the world, and its impact has been especially profound in the United States. It is now generally agreed that the fabric of shale systems comprise primarily of organic matter, inorganic material and natural fractures. However, the underlying flow mechanisms through these multi-porosity, multi-permeability systems are poorly understood. For instance, debate still exists about the predominant transport mechanism (diffusion, convection and desorption) as well as the flow interactions between organic matter, inorganic matter and fractures. Furthermore balancing the computational burden of precisely modeling the gas transport through the pores versus running full reservoir scale simulation is also contested. To that end, commercial reservoir simulators are developing new shale gas options but some, for expediency, rely on simplification of existing data structures and/or flow mechanisms. We present here the development of a comprehensive multi-mechanistic (desorption, diffusion and convection) multi-porosity (organic materials, inorganic materials and fractures), multi-permeability model that uses experimentally determined shale organic and inorganic material properties to predict shale gas reservoir performance. Our multi-mechanistic model takes into account gas transport due to both pressure-driven convection and concentration-driven diffusion. The model accounts for all the important processes occurring in shale systems, including desorption of multi-component gas from the organics surface, multi-mechanistic organic-inorganic material mass transfer, multi-mechanistic inorganic material-fracture network mass transfer, and production from a hydraulically fractured wellbore. Our results show that Dual-porosity Dual-permeability (DPDP) with Knudsen diffusion is generally adequate to model shale gas reservoir production. By comparing Triple-porosity Dual-permeability (TPDP), DPDP and Single-porosity Single-permeability (SPSP) formulations under similar conditions, we show that Knudsen diffusion is a key mechanism and should not be ignored. We also guide the fractures design by analyzing flow rate limiting steps. This work provides a basis for long-term shale gas production analysis and also helps define value-adding laboratory measurements.