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Brinkley, Kourtney (Devon Energy) | Ingle, Trevor (Devon Energy) | Haffener, Jackson (Devon Energy) | Chapman, Philip (Devon Energy) | Baker, Scott (Devon Energy) | Hart, Eric (Devon Energy) | Haustveit, Kyle (Devon Energy) | Roberts, Jon (Devon Energy)
Abstract This case study details the use of Sealed Wellbore Pressure Monitoring (SWPM) to improve the characterization of fracture geometry and propagation during stimulation of inter-connected stacked pay in the South Texas Eagle Ford Shale. The SWPM workflow utilizes surface pressure gauges to detect hydraulically induced fracture arrivals athorizontal monitor locations adjacent to the stimulated wellbore (Haustveit et al. 2020). A stacked and staggered development in Dewitt County provided the opportunity to jointly evaluateprimary completion and recompletion efforts spanning three reservoir target intervals. Fivemonitor wells at varying distances across the unit were employed for SWPM during the stimulation of four wells. An operational overview, analysis of techniques, correlation with seismic attributes, image log interpretations, and fracture model calibration are provided. Outputs from this workflow allow for a refined analysis ofthe overall completion strategy. The high-density, five well monitor array recorded a total of 160 fracture arrivals at varying vertical and lateral distances, with far-field fracture arrivalsprovidingsignificant insight into propagation rates and geometry. Apronounced trend occurred in both arrival frequency and volumes pumped as monitor locations increased in distance from the treatment well. Specific to target zone isolation, it was identified that traversing vertically in section through a high stress interval yielded a 30% reduction inarrival frequency. An indirect relationship between horizontal distance and arrival frequency was also observed when monitoring from the same interval. A decrease in fracture arrivals from 70% down to 8% was realized as offset distance increased from 120 to 1,700 ft. The results from this study have proven to be instrumental in guiding interdisciplinary discussion. Assessing fracture geometry and propagation during stimulation, particularly in the co-development of a stacked pay reservoir, is paramount to the determination of proper completion volume, perforation design, and well spacing. Leveraging the observations of SWPM ultimately provides greater confidence in field development strategy and economic optimization.
Seth, Puneet (The University of Texas at Austin) | Elliott, Brendan (Devon Energy Production Co. LP) | Ingle, Trevor (Devon Energy Production Co. LP) | Kumar, Ashish (The University of Texas at Austin) | Zheng, Shuang (The University of Texas at Austin) | Sharma, Mukul (The University of Texas at Austin)
In recent years, hydraulic fracturing design has evolved significantly. Fluid volumes injected in a lateral have more than doubled to ~ 250,000 bbl/well (
We analyze the pressure response in offset parent wells in the Permian Basin during treatment of a nearby well. Fracture arrival times (time taken for the child well fractures to intersect with a nearby parent well) are investigated for multiple stages during child well stimulation. This information from field data is coupled with fracture modeling simulations to evaluate if a frac-job is over-stimulated. Stages that suggest over-stimulation are systematically analyzed and dynamic plots are presented, that compare the capital efficiency at different times during a frac job.
Optimal completion designs that achieve uniform distribution of injected fluid volumes into multiple clusters are key to successful fracturing jobs. Simulations as well as field data show that if a large job volume is pumped with a sub-optimal completion design, fracture arrival at the offset well is early, leading to over-stimulation and wasted capital. In such a scenario, dominant clusters that take most of the injected fluid can intersect nearby offset well early in the treatment, while significantly bypassing undepleted reservoir rock along the lateral (that should ideally be drained) due to low cluster efficiency. This results in a frac job with poor capital efficiency and a well with lower productivity (due to interference with offset well). We show that early fracture arrival times are indicative of fewer clusters propagating fractures with very non-uniform fluid distribution (low cluster efficiency), whereas late arrival times indicate more uniform fluid distribution in the clusters with high cluster efficiency.
We introduce a novel technique that analyzes field offset well pressure data and fracture arrival times at the offset well to diagnose stimulation efficiency and prevent over-stimulation. Our method provides operators with a relatively inexpensive way to improve capital efficiency during a frac-job.
Infill development typically strives to improve resource recovery while maximizing economic objectives of the organization. Success is dependent on many variables, several of which include well spacing, completion design, and mechanical stratigraphy. Optimizing development is contingent upon understanding how these variables interact with one another and what combination of development strategies will maximize the company objective. One of the challenges with optimizing horizontal multi-frac wells has been quantifying well to well connectivity, understanding the appropriate amount, and how various development strategies impact that relationship. This paper will present a case for development optimization by integrating the results of multiple quantitative pressure interference tests with completion design and well spacing in the STACK play. The framework for quantifying the connectivity between wells was developed by Chu et al (2018) and is often referred to as Chow Pressure Group (CPG). Using this technique, the Magnitude of Pressure Interference (MPI) was quantified between 25 horizontal wells within 10 development units. The dataset is unique because the infill units were developed with varying completions and well spacings which provides an opportunity to isolate and understand how each variable directly impacts well to well connectivity. This study also addresses the desired amount of connectivity between horizontal wells and how it impacts well performance and recovery.
The results from this case study suggest there is a clear relationship between well spacing and MPI, consistent with the findings by Chu et al (2018). Ultimate recovery was investigated and found to have a correlation with the amount of connectivity between development wells. Additionally, at consistent well spacing, higher proppant volume per cluster increased MPI and Estimated Ultimate Recovery (EUR) per well. Increasing proppant per cluster is likely extending the conductive half-length, increasing fracture overlap and MPI, and reducing bypassed resource beyond the tips of the fractures, resulting in higher EUR and Drilling Spacing Unit (DSU) recovery.
This case study provides asset teams with valuable relationships between reservoir, completions, geologic characteristics and how they tie to well performance in the Anadarko Basin. These relationships are expected to be different in every basin/formation, however, it highlights the power of quantitative interference tests in optimizing infill development and understanding the appropriate amount of well to well connectivity. This work also lays out a practical example regarding the dependent nature of completions and reservoir well spacing which can serve as a workflow for asset teams working unconventional plays across the world.
Haustveit, Kyle (Devon Energy) | Elliott, Brendan (Devon Energy) | Haffener, Jackson (Devon Energy) | Ketter, Chris (Devon Energy) | O'Brien, Josh (Devon Energy) | Almasoodi, Mouin (Devon Energy) | Moos, Sheldon (Devon Energy) | Klaassen, Trevor (Devon Energy) | Dahlgren, Kyle (Devon Energy) | Ingle, Trevor (Devon Energy) | Roberts, Jon (Devon Energy) | Gerding, Eric (Devon Energy) | Borell, Jarret (Devon Energy) | Sharma, Sundeep (Devon Energy) | Deeg, Wolfgang (Formerly Devon Energy)
Over the past decade the shale revolution has driven a dramatic increase in hydraulically stimulated wells. Since 2010, hundreds of thousands of hydraulically fractured stages have been completed on an annual basis in the US alone. It is well known that the geology and geomechanical features vary along a lateral due to landing variations, structural changes, depletion impacts, and intra-well shadowing. The variations along a lateral have the potential to impact the fluid distribution in a multi-cluster stimulation which can impact the drainage pattern and ultimately the economics of the well and unit being exploited. Due to the lack of low-cost, scalable diagnostics capable of monitoring cluster efficiency, most wells are completed using geometric cluster spacing and the same pump schedule across a lateral with known variations.
A breakthrough patent-pending pressure monitoring technique using an offset sealed wellbore as a monitoring source has led to advancements in quantifying cluster efficiencies of hydraulic stimulations in real-time. To date, over 1,500 stages have been monitored using the technique. Sealed Wellbore Pressure Monitoring (SWPM) is a low-cost, non-intrusive method used to evaluate and quantify fracture growth rates and fracture driven interactions during a hydraulic stimulation. The measurements can be made with only a surface pressure gauge on a monitor well.
SWPM provides insight into a wide range of fracture characteristics and can be applied to improve the understanding of hydraulic fractures in the following ways: Qualitative cluster efficiency/fluid distribution Fracture count in the far-field Fracture height and fracture half-length Depletion identification and mitigation Fracture model calibration Fracture closure time estimation
Qualitative cluster efficiency/fluid distribution
Fracture count in the far-field
Fracture height and fracture half-length
Depletion identification and mitigation
Fracture model calibration
Fracture closure time estimation
The technique has been validated using low frequency Distributed Acoustic Sensing (DAS) strain monitoring, microseismic monitoring, video-based downhole perforation imaging, and production logging. This paper will review multiple SWPM case studies collected from projects performed in the Anadarko Basin (Meramec), Permian Delaware Basin (Wolfcamp), and Permian Delaware Basin (Leonard/Avalon).
Abstract Understanding the relative contribution of the rock and fluid characteristics versus the drilling and completion practices and their inter-connectedness nature on production performance of unconventional plays is key for successful decision making. Static and dynamic reservoir modeling is the most integrated approach for evaluating such a complex system, however, it becomes challenging when it comes to green fields with short production history where many uncertain parameters need to be quantified. Uncertainties associated with geological models (such as depositional direction, reservoir geometry, stratigraphy, etc.), reservoir parameters (such as permeability distribution, fluid characteristics and etc.) and hydraulic fracture geometries (fracture half-length and height) have led the petroleum industry towards stochastic approaches for static and dynamic reservoir modeling. Unlike deterministic methods, stochastic geological models are constructed based on interpretation and geostatistical simulation of different realizations of structural and petrophysical properties to cover the space of uncertainty within these parameters. Integrating these realizations into dynamic reservoir modeling coupled with other factors such as hydraulic fracture geometries and fluid characteristics produces a set of reasonable history matched models. Integration also provides a range of forecasts in the format of P-10, P-50 and P-90 that improves field development planning decisions. This study proposes a workflow that couples the static and dynamic stochastic models which were used to history match a multi-well pad with about one year of production history in the Meramec formation within the STACK (Sooner Trend Anadarko Canadian Kingfisher). All available data such as seismic, well logs, and core data were used to generate the base earth model (static model). To incorporate the geological and petrophysical uncertainties, multiple realizations were generated using the base earth model to account for the complexity in mapping the reservoir architecture and to address the lack of petrophysical data to distribute rock properties accurately. The P-10, P-50 and P-90 models of porosity, permeability and water saturation along with base fluid properties and hydraulic fracture geometries were used to build the base flow model. Using assisted history matching techniques production profiles were generated by running simulation models with the prior realizations of all parameters. Those production profiles that honor actual production history were selected to be forecasted to show the range of possible Estimated Ultimate Recovery (EUR). Furthermore, the P-10, P-50, and P-90 models had a no flow boundary imposed on them via grid slicing to obtain forecasts for multiple well spacing scenarios. These forecasts were then integrated into an economic model to study the impact various well spacing's scenarios have on economic metrics such as net present value. This information is utilized to aid in optimal development decisions.