Significant attention has been placed on well-to-well interactions since the beginning of infill development in unconventional plays. Asymmetry of hydraulic fractures toward pressure-depleted parent wells has been attributed to poor performance of early in-fill completions (Xuyang et al., 2019). Factors include well spacing, timing, and specifics of colocation. Various operators have developed strategies to mitigate these effects and improve production for new spacing units. This paper reviews the historical response to parent–child well development in the Bakken, characterizes trends, and examines the techniques that have been applied and their efficacy. Results provide evidence that development decisions continue to be largely driven by surface constraints, suggesting there is more work needed to improve future child well performance. There is a trend away from refracturing for most operators, particularly as part of drilling spacing unit (DSU) redevelopments involving drilling of multiple child wells. Common mitigation techniques include fracturing the child well closest to the parent well first and increasing well spacing.
This paper presents a workflow supported by field examples for modeling the Stimulated Reservoir Volume (SRV) as a Dynamic entity – constrained by validation or calibration against data from the frac treatment, flowback, production and pressure build-ups. The initial reservoir model of static properties is treated as a starting point and evolution of flow and storage are modeled as continuously variable properties using simultaneous modeling of flow and geomechanics – and hence the "dynamic" qualifier for SRV in the title of this paper. This workflow establishes the interaction between reservoir properties and completion parameters – and allows for program optimization. Emphasis is laid on (i) calibration with multiple types of independently measured field data points and (ii) construction of the simplest models which provide a useful degree of predictability, and we caution against over-parameterization through needless complexity of physical models. We first describe the calibration process – during which a model is perturbed to match various aspects of field data, and then explain the prediction process – where multiple hypothetical completion scenarios can be modeled. The results are then screened against economic metrics (e.g. completions costs vs. projected revenue from improved production) – reducing the number of hit-and-miss experiments in the field. While this paper is comprehensive and self-sufficient in its own coverage, it does focus on the physical modeling and calibration aspects, whereas a companion paper (Min et al. 2018), goes into greater detail about the predictive aspects and data analytics driven completion optimization.
A novel DFIT simulator comprising a 3D hydraulic fracturing model seamlessly coupled within one software with reservoir flow and geomechanical modeling is described and used to numerically analyze DFITs in unconventional reservoirs. This workflow involves history matching treatment or injection pressures (fracture propagation) and shut-in (fracture closure) pressures consistent with 3D growth of hydraulic fractures in the presence of pressure dependent leak-off. These are the same fundamental processes which characterize Dynamic Stimulated Reservoir Volume or DSRV growth (Sen et al., 2018, Min et al., 2018) and DFITs can therefore be used to get a better early prognosis on the potential of DSRV growth in a tight reservoir. This modular DFIT simulator iteratively couples a finite-difference reservoir simulation with a finite- element geomechanical modeling within one software and can therefore maintain important consistencies between fracture opening, propagation, closure and the stress dependent leak-off and permeability evolution inside the induced dynamic SRV. Both DFIT injection and closure processes are numerically modeled - and depending on which model parameters we choose to fix and which we perturb, we can preemptively estimate the potential for a successful stimulation and its possible dimensions. This estimate can be obtained at the early stages of a field /section development, before embarking on major drilling and completion campaigns, even in the absence of substantial production data. And it provides guidance for optimizing major fracturing design and well spacing. This approach is not reliant or bound by the assumptions underlying widely-used analytical DFIT analyzing methods, and is therefore more flexible and better captures the physics of stimulation in unconventional reservoirs. An early understanding of the key geomechanical metrics defining unconventional reservoir enhancement (DSRV effectiveness) allows us to build a directional relationship between fracturing parameters and post-fracture production without the need for an extended record of production trends. This speeds up the continuous learning and adaptive process of completion optimization involving pumped volumes, cluster spacing and well landing zones.
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