Recent examples of hydraulic-fracture diagnostic data suggest that complex, multistranded hydraulic-fracture geometry is a common occurrence. This reality is in stark contrast to the industry-standard design models based on the assumption of symmetric, planar, biwing geometry. The interaction between pre-existing natural fractures and the advancing hydraulic fracture is a key condition leading to complex fracture patterns. Performing hydraulic-fracture-design calculations under these less-than-ideal conditions requires modeling fracture intersections and tracking fluid fronts in the network of reactivated fissures. Whether a hydraulic fracture crosses or is arrested by a pre-existing natural fracture is controlled by shear strength and potential slippage at the fracture intersections, as well as potential debonding of sealed cracks in the near-tip region of a propagating hydraulic fracture. We present a complex hydraulicfracture pattern propagation model based on the extended finiteelement method (XFEM) as a design tool that can be used to optimize treatment parameters under complex propagation conditions. Results demonstrate that fracture-pattern complexity is strongly controlled by the magnitude of anisotropy of in-situ stresses, rock toughness, and natural-fracture cement strength, as well as the orientation of the natural fractures relative to the hydraulic fracture. Analysis shows that the growing hydraulic fracture may exert enough tensile and shear stresses on cemented natural fractures that the latter may be debonded, opened, and/or sheared in advance of hydraulic-fracture-tip arrival, while under other conditions, natural fractures will be unaffected by the hydraulic fracture. Detailed aperture distributions at the intersection between fracture segments show the potential for difficulty in proppant transport under complex fracture-propagation conditions.
Fluid flow in fractured porous media has always been important in different engineering applications especially in hydrology and reservoir engineering. However, by the onset of the hydraulic fracturing revolution, massive fracturing jobs have been implemented in unconventional hydrocarbon resources such as tight gas and shale gas reservoirs that make understanding fluid flow in fractured media more significant. Considering ultralow permeability of these reservoirs, induced complex fracture networks play a significant role in economic production of these resources. Hence, having a robust and fast numerical technique to evaluate flow through complex fracture networks can play a crucial role in the progress of inversion methods to determine fracture geometries in the subsurface. Current methods for tight gas flow in fractured reservoirs, despite their advantages, still have several shortcomings that make their application for real field problems limited. For instance, the dual permeability theory assumes an ideal uniform orthogonal distribution of fractures, which is quite different from field observation; on the other hand, numerical methods like discrete fracture network (DFN) models can portray the irregular distribution of fractures, but requires massive mesh refinements to have the fractures aligned with the grid/element edges, which can greatly increase the computational cost and simulation time. This paper combines the extended finite element methods (XFEM) and the gas pseudo-pressure to simulate gas flow in fractured tight gas reservoirs by incorporating the strong-discontinuity enrichment scheme to capture the weak-discontinuity feature induced by highly permeable fractures. Utilizing pseudo-pressure formulations simplifies the governing equations and reduces the nonlinearity of the problem significantly. This technique can consider multiple fracture sets and their intersection to mimic real fracture networks on a plain structured mesh. Here, we utilize the unified Hagen–Poiseuille-type equation to compute the permeability of tight gas, and finally adopt Newton–Raphson iteration method to solve the highly nonlinear equations. Numerical results illustrate that XFEM is considerably effective in fast calculation of gas flow in fractured porous media.
Summary The unsteady recovery of oil and gas prices in early 2017 led to an increase in drilling and hydraulic–fracturing operations in liquid–rich shale plays in North America. As field–development strategies continue to evolve, refracturing and infill–well drilling must be carefully combined to optimize shale–project profitability. Moreover, operators must bear in mind the undulating natural–gas demands persisting in an oversupplied shale–gas environment. In this paper, we use data–driven approaches to predict successful refracturing candidates and local gas demand for the second–tier optimization of a shale–gas supply–chain network. A strategic–planning (SP) model is developed for optimizing the net present value (NPV) of a case–study shale–gas network in the Marcellus Play. This SP model uses a mixed–integer–nonlinear–programming (MINLP) formulation developed in the General Algebraic Modeling System (GAMS, Release 27.1.0.2019). This model relies directly on input from reservoir simulation, local–gas–demand forecast, water–availability forecast, and natural–gas and West Texas Intermediate (WTI) crude–oil price forecasts. Before reservoir simulation, machine learning (ML) is used to predict successful refracturing candidates, using a feed–forward neural network (NN), random–forest (RF) classifier, and a t–distributed stochastic–neighbor–embedding (t–SNE) visualization technique. Using the obtained results, best–practice field–development strategies are implemented in the area of interest (AOI) using reservoir simulation. Local gas demand is forecasted using a long–short–term–memory (LSTM) recurrent NN (RNN) that uses a multivariate data set created from local and global variables affecting shale–gas demand. A water–management structure is also developed for the optimization framework. Using a 300–well data set (with 17 input features), successful refracturing candidates were proposed according to the joint outcome of an optimal 17/23/128/2 feed–forward NN, a t–SNE plot, and a techno–economic review. After ranking F1 scores, the developed NN outperforms the RF and support–vector–machine (SVM) algorithms for frac/refrac–well classification. The developed 32/256/128/120 LSTM model showed at least a 93% (±1%) prediction performance using three or five input features. The results illustrate the ability of the developed LSTM model to accurately predict local gas demands during periods of high or low gas demand. After SP optimization over a 10–year planning horizon, the economic results indicate an NPV of USD 481.945 million, using the proposed physics–data–driven–based approach. An NPV of USD 611.22 million is obtained when no ML was used. The results reveal that the application of ML to strategic planning can prevent erroneous feedback of project profitability while allowing early–time decision making that maximizes shale–asset NPV.
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