This paper gives a general review of the application of global optimization methods to reservoir description problems. The important parameters that control the performance of these algorithms are described and illustrations of the effect on the convergence are protilded. A "detailed review of past applications is presented and used to illustrate the fact that current computer technology is sufficient. Several current applications of global optimization? other_ than stochastic modeling, are used to emphas]ze the benefits of these methods. With
A new approach that combines the use of continuum and discrete fracture modeling methods has been developed. The approach provides the unique opportunity to constrain the fractured models to all existing geologic, geophysical, and engineering data, and hence derive conditioned discrete fracture models. Such models exhibit greater reality, since the spatial distribution of fractures reflects the underlying drivers that control fracture creation and growth. The modeling process is initiated by constructing continuous fracture models that are able to capture the underlying complex relationships that may exist between fracture intensity (defined by static measures, such as fracture count, or dynamic measures, such as hydrocarbon production), and many possible geologic drivers (e.g. structure, thickness, lithology, faults, porosity). Artificial intelligence tools are used to correlate the multitude of geologic drivers with the chosen measure of fracture intensity. The resulting continuous fracture intensity models are then passed to a discrete fracture network (DFN) method. The current practice in DFN modeling is to assume fractures are spatially distributed according to a stationary Poisson process, simple clustering rules, or controlled by a single geologic driver. All these approaches will in general be overly simplistic and lead to unreliable predictions of fracture distribution away from well locations. In contrast, the new approach determines the number of fractures in each grid-block, based on the value of the fracture intensity provided by the continuous model. As a result, the discrete fracture models honor all the geologic conditions reflected in the continuous models and exhibit all the observed fracture features. The conditioned DFN models are used to build a realistic and detailed model of flow in discrete conduits. There are two main areas where detailed discrete fracture models can be used:Upscaling of fracture properties (permeability, porosity and s factor) for input into reservoir simulators; andOptimization of well-design, completion and operation based on an understanding of the inter-well scale flows. For accurate results, the full permeability tensor is calculated for each grid-block based on flow calculations using generalized linear boundary conditions. Inter-well flows are analyzed in terms of the variability in flow paths, characterized by distance and time traveled, through the fracture network connecting injectors and producers. Introduction Many large oil and gas fields in the most productive regions such as the Middle East, South America, and Southeast Asia happen to be fractured. The exploration and development of such reservoirs is a true challenge for many operators who do not possess the tools and technology to completely understand and predict the effects of fractures on the overall reservoir behavior. Although many fractured reservoirs could be developed economically, it is very common to see operators abandoning these fields because of their inability to drill wells that intercept fractures, and/or inability to estimate correctly reservoir pressure during a pressure transient test. After many years, if not decades, of missed opportunities, the petroleum industry is realizing the need for better fractured reservoir modeling tools.
We have developed a new geomechanical workflow to study the mechanics of hydraulic fracturing in naturally fractured unconventional reservoirs. This workflow used the material point method (MPM) for computational mechanics and an equivalent fracture model derived from continuous fracture modeling to represent natural fractures (NFs). We first used the workflow to test the effect of different stress anisotropies on the propagation path of a single NF intersected by a hydraulic fracture. In these elementary studies, increasing the stress anisotropy was found to decrease the curving of a propagating NF, and this could be used to explain the observed trends in the microseismic data. The workflow was applied to Marcellus and Eagle Ford wells, where multiple geomechanical results were validated with microseismic data and tracer tests. Application of the workflow to a Marcellus well provides a strain field that correlates well with microseismicity, and a maximum energy release rate, or [Formula: see text] integral at each completion stage, which appeared to correlate to the production log and could be used to quantify the impact of skipping the completion stages. On the first of two Eagle Ford wells considered, the MPM workflow provided a horizontal differential stress map that showed significant variability imparted by NFs perturbing the regional stress field. Additionally, a map of the strain distribution after stimulating the well showed the same features as the interpreted microseismic data: three distinct regions of microseismic character, supported by tracer tests and explained by the MPM differential stress map. Finally, the workflow was able to estimate, in the second well with no microseismic data, its main performance characteristics as validated by tracer tests. The field-validated MPM geomechanical workflow is a powerful tool for completion optimization in the presence of NFs, which affect in multiple ways the final outcome of hydraulic fracturing.
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