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Continuous improvement of the completion design in horizontal wells is the key to improve the ultimate recovery from shale resources. Accounting for not only the geological characteristics of the target formation but also the spatial heterogeneity in the target layer is a significant step in achieving the optimum completion design and improving the production efficiency. For this purpose, the present study proposes a comprehensive descriptive data analytics workflow using the completion design and reservoir metrics of more than 400 fracturing stages from the eleven horizontal Wolfcamp wells in the Permian Basin at the hydraulic fracturing test site (HFTS).
In this study, fracture gradient, calculated based on the measured instantaneous shut-in pressure (ISIP), is utilized as the reservoir response to the hydraulic fracturing work. The proposed workflow evaluates the impact of variations in the reservoir properties and completion design parameters on the reservoir response to the hydraulic fracturing process. It also facilitates explaining the variations in the production performance of the horizontal wells placed in the same formation. The impact of added fracture complexity in the presence of active or inactive vertical producers located within a certain distance from the horizontal wells is also evaluated. A supervised multivariate analysis is used in this work to provide an insight into the importance of selecting the optimum completion design on a well by well basis, highlighting the importance of adapting the design of fracturing stages to the variations of the formation properties along the lateral placements of horizontal wells.
Results indicate that the best performing wells, from the cumulative oil production standpoint, are those that experienced changes in the stage completion and treatment parameters compatible with the inverted reservoir properties variations. It is also observed that in the upper Wolfcamp, formation properties dominantly control the zonal fracture gradients while in the middle Wolfcamp, completion design parameters are the dominant controllers. This workflow is used for the first time to explain the possible causes of variations in the production performance of the similarly designed HFTS wells in the Wolfcamp formation.
This study presents the application of a data-driven workflow for evaluating the completion design and production performance of the horizontal Wolfcamp wells located in the Midland Basin at the Hydraulic Fracturing Test Site (HFTS1). Leveraging the diverse and comprehensive datasets available at HFTS, the impact of various factors including completion design, reservoir properties, well spacing, and geospatial distribution of more than 400 hydraulic fracturing stages on the well performance is evaluated.
The proposed workflow assesses the impact of variations in the reservoir properties and completion design parameters on the formation response to the hydraulic fracturing work as well as production performance. It exhibits that the fracturing gradients calculated based on the measured instantaneous shut-in pressures (ISIP) are good indicators of the formation heterogeneity along the laterals in both the upper and middle Wolfcamp formations. Fracturing gradients are strongly correlated with both reservoir properties and well treatment factors and production performances are highly impacted by the inter-well communications resulted from the fracturing behavior.
The supervised multivariate analysis in this work provides an insight into the importance of selecting the optimum completion design on a well by well basis, highlighting the importance of adapting the design of hydraulic fracturing stages to the formation characteristics along the lateral placements of the horizontal wells by adjusting the perforation densities and proppant load. It also indicates that the presence of the offset verticals contributes to the fracture network complexity which positively impacts the ultimate fracturing potential in the nearby stages. Results suggest that aggressive stimulation in the regions with a higher range of fracturing gradient and higher clay content adversely impacted the production performance. It is also observed that the best performing wells, from the oil production standpoint, are those that experienced completion and treatment variations compatible with the formation characteristics along the laterals and improved fracturing techniques.
Four main categories of data are used in this workflow including formation parameters, completion design attributes, geospatial distribution of hydraulic fracturing stages, and the formation response to the hydraulic fracturing work. This workflow utilizes data from different disciplines to explain how different parameters can impact the production behavior of a well.
Abstract Callovian-Kimmeridgian organic-rich carbonates (Hanifa Formation and equivalents) are exceptional source rocks that have generated substantial volumes of hydrocarbons and charged prolific conventional reservoirs across the Middle East. This stratigraphic interval is now also under appraisal as an unconventional play with a vast resource potential. An unconventional screening workflow, assessing organic-carbon content, maturity, thickness, and depth, has identified a considerable area that appears to be viable as an exploration target. To gain an understanding of the controls on sweet-spot distribution in this frontier unconventional play, it is necessary to consider a producing analogue. The Eagle Ford play in the Western Gulf, USA is commonly considered as a reservoir completion analogue because it has a comparable carbonate-dominated composition. However, the Eagle Ford play does not appear to be a pertinent exploration analogue because the geological criteria that control the distribution of sweet spots are fundamentally distinct from the Hanifa play. In this study, the emerging Vaca Muerta play in the Neuquén Basin, Argentina is considered to be a useful exploration analogue because its depositional architecture, stratigraphic variability, and composition are comparable to the Hanifa play. Sweet spots in the Vaca Muerta play are controlled by mechanical stratigraphy, which is related to the architecture and stratigraphic variability within the depositional system. Interval-specific production data from the Vaca Muerta Formation demonstrates that the best-performing units are not necessarily the most organic-rich, but relate to units with high frequency, cyclical intercalation of organic-rich units, and more brittle carbonate-dominated target horizons. The integration of seismic, well log, geomechanical, and production data demonstrates that sweet spots occur within progradational packages on the carbonate ramp. The best-performing areas (e.g., northwest sector at Loma Campana Block) intersect the lowstand systems tract where forced regression of the carbonate ramp induces reworking and detrital carbonate input into the anoxic basin. By upscaling these concepts, an unconventional exploration model can be formulated to guide regional sweet-spot prediction. The unconventional exploration model uses gross depositional environment maps, within the predictive framework of a sequence stratigraphic model, to identify the aerial extent of geomechanical sweet spots within each defined eustatic sequence. This is a valuable tool that can be used in conjunction with regional seismic data to identify potential sweet spots in both the Vaca Muerta play and the analogous Hanifa play.
Abstract Microseismic monitoring of hydraulic fracturing in unconventional reservoirs is a valuable tool for delineating the effectiveness of stimulations, completions, and overall field development. Important information, such as fracture azimuth, fracture length, height growth, staging effectiveness, and many other geometric parameters, can typically be determined from good quality data sets. In addition, there are parameters now being extracted from microseismic data sets, or correlated with microseismic data, to infer other properties of the stimulation/completion system, such as stimulated reservoir volume (SRV), discrete fracture networks (DFNs), structural effects, proppant placement, permeability, fracture opening and closure, geohazards, and others. Much of the information obtained in this way is based on solid geomechanical or seismological principles, but some of it is speculative as well. This paper reviews published data where microseismic results have been validated by experiments using some type of ground-truth or alternative measurement procedure, discusses the geomechanics and seismological mechanisms that can be reasonably considered in evaluating the likelihood of inferring given properties, and appraises the uncertainties associated with monitoring and the effect on any inferences about fracture behavior. Considerable data now exist from tiltmeters, fiber-optic sensing, tracers, pressure sensors, multi-well-pad experiments, and production interference that can be used to aid the validation assessment. Relatively limited microseismic results have actually been validated in any consistent manner. Fracture azimuth from microseismic has been verified across a wide range of reservoir types using multiple techniques. Good validation of fracture length and height were performed in sandstones for planar fractures; fracture length and height in typical horizontal completions with multiple fractures or complexity have a lesser degree of verification. Other parameters, such as complexity, discrete fracture networks, source parameters, and SRV, have little supporting evidence to provide validation, even though they might have sound physical principles underlying their application. It is clear that microseismic monitoring would benefit from more attention to validation testing. In many cases, the data might be available but have not been used for validation purposes, or such results have not been published.
Efficient and cost-effective unconventional oil and gas (UOG) recovery depends critically on the knowledge of primary factors controlling the reservoir producing behaviors, as well as on well completion strategies. Currently the completion design of UOG wells is often dominated by geometry-based approaches, neglecting the impact of spatial heterogeneity of reservoir properties. The primary goal of this work is to identify geological factors and well completion strategies important to production using systematic Design of Experiment (DoE) methodologies, and then train a data-driven, machine-learning (ML) proxy model to expedite optimization of well completion. The results are demonstrated for applications in Permian Basin.
A set of Permian Basin wells are selected to provide a wide spectrum of geological, geomechanical and completion features existing in the basin. For each well, process-level modeling is performed in commercial hydraulic fracturing (HF) and reservoir simulators. For each well, the HF model is calibrated against historical well production data, by adjusting hydraulic fracture structure and reservoir properties. Using DoE methodologies, we evaluate a large number of completion strategies, in conjunction to different geological and geomechanical conditions. The effect of different decision variables on HF completion efficiency and production are examined, including the type of proppant carrier fluid (slick water and crosslinker), and proppant types (e.g., ceramic, curved resin, sizes, concentration, size, etc.). The results are used to develop an ML-based proxy model, which can be used to make rapid design of well completion strategies for future development, without requiring running time-consuming, full-scale reservoir simulations.
Simulation results of this work show the well completion implementation for many of the selected wells is far from optimal. History matching helps to establish the input-production relationships for each well, which provides a base model for sensitivity runs. The well set is divided into two parts. One part is used to develop an ML proxy model, and the rest is used for testing. Proxy modeling results suggest that the machine learning model can learn the complex reservoir input-output relations well, providing a data-driven tool for rapid well completion design and field production evaluation.
The complex geology, coupled with dynamic fracture stimulation and reservoir production processes are often not well investigated and represented in current practice, due to the lack of tools and representative data. As a result, sub-optimal production prevails, which erodes the HF economics. Previous works either mainly focus on mechanism simulation study or over-emphasize the original raw data but neglect the complex geology and physics underlying fracturing and reservoir production processes. The contribution of this study is we provide an integrated solution for well completion design based on real production data, fracturing simulation and reservoir simulation from different representative locations in Permian Basin. The physics-based proxy model can be used for future well designs by taking account into the effects of a large number of geological, geomechanical, and production factors.