A design of hydraulic fracturing in variably-stressed zones is one of key components for an effective multi-zone, multi-horizontal well pad treatment. In the recent literature, optimum completion strategies catering for stimulation-induced in-situ stress changes are discussed, however, only few of these focus on vertical stress changes and its impact on multi-zone fracture geometries. In this paper, we present an approach to design contained hydraulic fractures in a high stress layers by studying the role of vertical stress shadowing on actual field data. In modeling hydraulic fractures with pseudo-3D models, if fracture simulations are initiated in high stress zones, "artificially" unbounded height growth results in very limited lateral propagation. On the other hand, 3D hydraulic fracturing models are too computationally expensive to optimize large design jobs, for example, in multi-horizontal well pads. In this paper, we employ a Stacked Height Growth Model, whereby fractures are also discretized vertically yet retain the numerical formulation pseudo-3D models. Coupling with finite element stress solvers then allows to identify vertical stress changes in the vicinity of induced hydraulic fractures and to understand the interference between hydraulic fracture sequences and their respective microseismic signatures. Considering a potential combination of fracturing sequences, it was revealed that stress perturbations from the neighboring well hydraulic fractures initiating from low stress layers can be used to increase stress within the same zone and also potentially reduce stresses in higher-stress layers above and below. By modeling and calibrating an actual multi-zone, multi-horizontal stimulation job, we elaborate on the benefits of increasing stress barriers before fracturing in higher-stress layer to avoid the chances of re-fracturing from high stress zones. Regarding hydraulic fracture geometries, we explain our results by analyzing actual microseismic observations with respect to simulated stress patterns after stimulation. We explore the notion of deliberately ordering hydraulic fracture to manage vertical interference and create more contained fractures in a multi-zone horizontal well pad. Fracturing in a higher-stress zone will naturally divert the energy into low stress, potentially unproductive zones. In an effort to manage this phenomenon, this paper presents one of the few data-rich case studies on multi-zone, multi-well engineered stimulation design. The approach shown in this paper can be a helpful reference to understand fracture height growth in the presence of both vertical and horizontal stress shadowing.

Reservoir stimulation by means of hydraulic fracturing has enhanced the production of hydrocarbons from shale. The latest methods concentrate on reducing the detrimental interference between fractures within the same well. The theory behind this is well understood, however the concept of constructive interference between fractures in adjacent wells is not so developed.In this paper, contemporary hydraulic fracturing techniques are numerically simulated to analyze both the rock and fluid mechanic effects on the generation of fractures within a reservoir. The techniques involve various geometrical patterns of well and fracture spacing, followed by methods of sequencing the different stages of stimulation. These methods aim to reduce the compressive stress normal to fractures and increase the size o f tensile regions between fractures in adjacent wells. To measure the performance of different methods, the simulated microseismic energy released is used to calculate the stimulated rock volume (SRV). Production profiles have been generated for several key hydraulic fracturing scenarios, with the results predominantly showing an optimum well spacing to exist.

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Summary In this paper, we propose a methodology that combines finite-element modeling with neural networks in the numerical modeling of systems with behavior that involves a wide span of spatial scales. The method starts by constructing a high-resolution model of the subsurface, including its elastic mechanical properties and pore pressures. A second model is also constructed by scaling up mechanical properties and pressures into a coarse spatial resolution. Inexpensive finite-element solutions for stress are then obtained in the coarse model. These stress solutions aim at capturing regional trends and large-scale stress correlations. Finite-element solutions for stress are also obtained in high resolution, but only in a small subvolume of the 3D model. These stress solutions aim at estimating fine-grained details of the stress field introduced by the heterogeneity of rock properties at the fine scale. A neural network is then trained to infer the transformation rules that map stress solutions between different scales. The inputs to the training are pressure and mechanical properties in high and low resolutions. The output is the fine-scale stress computed in the subvolume of the high-resolutionmodel. Once trained, the neural network can be used to approximate a high-resolution stress field in the entire 3D volume using the coarse-scale solution and only providing high-resolution material properties and pressures. The results obtained indicate that when the coarse finite-element solutions are combined with the neural-network estimates, the results are within a 2 to 4% error of the results that would be computed with high-resolutionfinite-element models, but at a fraction of the cost in time and computational resources. This paper discusses the benefits and drawbacks of the method and illustrates its applicability by means of a worked example.

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