The DFIT flowback analysis (DFIT-FBA) method, recently developed by the authors, is a new approach for obtaining minimum in-situ stress, reservoir pressure, and well productivity index estimates in a fraction of the time required by conventional DFITs. The goal of this study is to demonstrate the application of DFIT-FBA to hydraulic fracturing design and reservoir characterization by performing tests at multiple points along a horizontal well completed in an unconventional reservoir. Furthermore, new corrections are introduced to the DFIT-FBA method to account for perforation friction, tortuosity, and wellbore unloading during the flowback stage of the test. The time and cost efficiency associated with the DFIT-FBA method provides an opportunity to conduct multiple field tests without delaying the completion program. Several trials of the new method were performed for this study. These trials demonstrate application of the DFIT-FBA for testing multiple points along the lateral of a horizontal well (toe stage and additional clusters). The operational procedure for each DFIT-FBA test consists of two steps: 1) injection to initiate and propagate a mini hydraulic fracture and 2) flowback of the injected fluid on surface using a variable choke setting on the wellhead. Rate transient analysis methods are then applied to the flowback data to identify flow regimes and estimate closure and reservoir pressure. Flowing material balance analysis is used to estimate the well productivity index for studied reservoir intervals. Minimum in-situ stress, pore pressure and well productivity index estimates were successfully obtained for all the field trials and validated by comparison against a conventional DFIT. The new corrections for friction and wellbore unloading improved the accuracy of the closure and reservoir pressures by 4%. Furthermore, the results of flowing material balance analysis show that wellbore unloading might cause significant over-estimation of the well productivity index. Considerable variation in well productivity index was observed from the toe stage to the heel stage (along the lateral) for the studied well. This variation has significant implications for hydraulic fracture design optimization, particularly treatment pressures and volumes.

The industry is facing significant challenges due to the recent downturn in oil prices, particularly for the development of tight reservoirs. It is more critical than ever to 1) identify the sweet spots with less uncertainty and 2) optimize the completion-design parameters. The overall objective of this study is to quantify and compare the effects of reservoir quality and completion intensity on well productivity. We developed a supervised fuzzy clustering (SFC) algorithm to rank reservoir quality and completion intensity, and analyze their relative impacts on wells' productivity. We collected reservoir properties and completion-design parameters of 1,784 horizontal oil and gas wells completed in the Western Canadian Sedimentary Basin. Then, we used SFC to classify 1) reservoir quality represented by porosity, hydrocarbon saturation, net pay thickness and initial reservoir pressure; and 2) completion-design intensity represented by proppant concentration, number of stages and injected water volume per stage. Finally, we investigated the relative impacts of reservoir quality and completion intensity on wells' productivity in terms of first year cumulative barrel of oil equivalent (BOE). The results show that in low-quality reservoirs, wells' productivity follows reservoir quality. However, in high-quality reservoirs, the role of completion-design becomes significant, and the productivity can be deterred by inefficient completion design. The results suggest that in low-quality reservoirs, the productivity can be enhanced with less intense completion design, while in high-quality reservoirs, a more intense completion significantly enhances the productivity. Keywords Reservoir quality; completion intensity; supervised fuzzy clustering, approximate reasoning,tight reservoirs development

A seven-step workflow to help subsurface teams establish an initial thesis for optimal completion design (cluster spacing, proppant per cluster) and well spacing in emerging / under-explored resource plays is proposed and executed for the Powder River Basin Niobrara unconventional oil play. The workflow uses Rate Transient Analysis (RTA) to determine the A c ∗ k parameter and then walks the reader through how to sequentially decouple the parameter into its constituent parts (frac height (h), number of symmetrical fractures achieved (n f ), permeability (k) and fracture half-length (x f )). Once these terms were quantified for each of the case study wells, they were used in a black oil reservoir simulator to compare predicted verses actual cumulative oil performance at 30, 60, 90,120 & 180 days. A long-term production match was achieved using x f as the lone history match parameter. x f verses proppant per effective half-cluster yielded an R 2 value of > 0.90. 28 simulation scenarios were executed to represent a range of cluster spacing, proppant per cluster and well spacing scenarios. Economics (ROR and/or NPV10/Net Acre) were determined for each of these scenarios under three different commodity pricing assumptions ($40/$2.50, $50/$2.50 and $60/$2.50). An initial thesis for optimal cluster spacing, proppant per designed cluster and well spacing were determined to be 12’, 47,500 lbs and 8-14 wells per section (based on whether or not fracture asymmetry is considered) when WTI and Henry Hub are assumed to be $50 & $2.50 flat.

With a recent trend in increased infill well development in the Midland basin and other unconventional plays, it has been shown that depletion has a significant impact on hydraulic fracture propagation. This is largely because production drawdown causes in-situ stress changes, resulting in asymmetric fracture growth toward the depleted regions. In turn, this can have a negative impact on production capacity. For the initial part of this study, an infill child well was drilled and completed adjacent to a parent well that had been producing for two years. Due to drilling difficulties, the child well was steered to a new target zone located 125 feet above the original target. However, relative to the original target, treatment data from the new zone indicated abnormal treatment responses leading to a study to evaluate the source of these variations and subsequent mitigation. The initial study was conducted using a pore pressure estimation derived from drill bit geomechanics data to investigate depletion effects on the infill child well. The pore pressure results were compared to the child well treatment responses and bottom hole pressure measurements in the parent well. Following the initial study, additional hydraulic fracture modeling studies were conducted on a separate pad to investigate depletion around the infill wells, determine optimal well spacing for future wells given the level of depletion, and optimize treatment designs for future wells in similar depletion scenarios. A depletion model workflow was implemented based on integrating hydraulic fracture modeling and reservoir analytics for future infill pad development. The geomechanical properties were calibrated by DFIT results and pressure matching of the parent well treatments for the in-situ virgin conditions. Parent well fracture geometries were used in an RTA for an analytical approach of estimating drainage area of the parent wells. These were then applied to a depletion profile in the hydraulic fracture model for well spacing analysis and treatment design sensitivities. Results of the initial study indicated that stages in the new, higher interval had higher breakdown pressures than the lower interval. Additionally, the child well drilled in the lower interval had normal breakdown pressures in line with the parent well treatments. This suggests that treatment differences in the wells were ultimately due to depletion of the offset parent well. Based on the modeling efforts, optimal infill well spacing was determined based on the on-production time of the parent wells. The optimal treatment designs were also determined under the same conditions to minimize offset frac hits and unnecessary completion costs. This case study presents the use of a multi-disciplinary approach for well spacing and treatment optimization. The integration of a novel method of estimating pore pressure and depletion modeling workflows were used in an inventive way to understand depletion effects on future development.

Fracture growth in layered formations with depth-dependent properties has been a topic of interest amongst researchers because of its critical influence on well performance. This paper revisits some of the existing height-growth models and discusses the evaluation process of a new and modified model developed after incorporating additional constraints.The net-pressure is the primary driver behind fracture propagation and the pressure distribution in the fracture plays an important role in vertical propagation, as it supplies the necessary energy for fracture advancement in the presence of opposing forces. The workflow adopted for this study included developing a preliminary model that solves a system of non-linear equations iteratively to arrive at fracture height versus net pressure mapping. The theoretical results were then compared to those available in the literature. The solution set was then extended to a 100-layer model after incorporating additional constraints using superposition techniques.The predicted outcomes were finally compared to the fracture height observations made in the field on several treatments. A reasonable agreement between model-predicted and observed height was observed when a comparison between the two was made, for most cases.The majority of these treatments were pumped in vertical wells, at low injection rates of up to 8.0 bbl/min (0.021 m 3 /s) where net pressures were intentionally restricted to 250 psi (1.72 MPa) in order to prevent fracture rotation to the horizontal plane.The leak-off was minimal given the low permeability formations. In some cases, however, the pumping parameters and fluid imparted pressure distribution appeared to dominate. Overall, it was apparent that for a slowly advancing fracture front, which is the case in low injection rate treatments, the fracture height could be predicted with reasonable accuracy. This condition could also be met in high rate treatments pumped down multiple perforation clusters such as in horizontal wells, though fracture-height measurement may not be as straightforward as in vertical wells. The model developed under the current study is suitable for vertical wells where fracture treatments are pumped at low injection rates. The solid-mechanics solution that is presented here is independent of pumping parameters and can be readily implemented to assist in selection of critical design parameters prior to the job, with a wide range of applicability worldwide.

Maximum horizontal stress (S H ) and stress path (change of S H and minimum horizontal stress with depletion) are the two most difficult parameters to define for an oilfield geomechanical model. Understanding these in-situ stresses is critical to the success of operations and development, especially when production is underway, and the reservoir depletion begins. This paper introduces a method to define them through the analysis of actual minifrac data. Field examples of applications on minifrac failure analysis and operational pressure prediction are also presented. It is commonly accepted that one of the best methods to determine the minimum horizontal stress (S h ) is the use of pressure fall-off analysis of a minifrac test. Unlike S h , the magnitude of S H cannot be measured directly. Instead it is back calculated by using fracture initiation pressure (FIP) and S h derived from minifrac data. After non-depleted S h and S H are defined, their apparent Poisson's Ratios (APR) are calculated using the Eaton equation. These APRs define S h and S H in virgin sand to encapsulate all other factors that influence in-situ stresses such as tectonic, thermal, osmotic and poro-elastic effects. These values can then be used to estimate stress path through interpretation of additional minifrac data derived from a depleted sand. A geomechanical model is developed based on APRs and stress paths to predict minifrac operation pressures. Three cases are included to show that the margin of error for FIP and fracture closure pressure (FCP) is less than 2%, fracture breakdown pressure (FBP) less than 4%. Two field cases in deep-water wells in the Gulf of Mexico show that the reduction of S H with depletion is lower than that for S h .

Assessment of in-situ stresses and hydraulic fracturing stimulation are two critical parameters for successful heat extraction from Enhanced Geothermal Systems (EGS). Fracture injection and injection/flow back tests are two conventional techniques for estimating the minimum horizontal stress in subsurface formations. Because of the heat exchange during the test, ultra-low permeability of the host rock, and natural fractures, the conventional methods yield inaccurate results in geothermal reservoirs. In this paper, we present a new methodology based on the signal processing approach for analyzing DFIT in geothermal reservoirs. The applicability of our technique is demonstrated using several test data from the Utah FORGE project. The main advantage of our methodology is that it does not depend on any assumption regarding fracture geometry and rock properties. Also, unlike most similar studies, we consider the effect of heat exchange between fracturing fluid and the hot rock. In our methodology, the recorded pressure and temperature are treated as signals, and a wavelet transform is applied to separate them to high pass (noise) and low pass (approximation) components. Using the noise energy of the two signals, we then identify different events such as fracture closure. Also, an analytical technique is used to correct the pressure by extracting the effect of fluid compressibility and heat exchange between the rock and injected fluid. We show that the G-Function technique underestimates the minimum horizontal stress in tight formations. After applying the corrections for pressure, the underestimation becomes more apparent. However, our approach gives consistent results before and after the pressure correction. Using the developed technique, we analyzed several injection tests from the Utah FORGE project. Both recorded pressure and temperature have been analyzed. Results show that the energy of the pressure signal noise decreases to a minimum level at the fracture closure. The fracture closure is confirmed by applying the same technique on the recorded temperature. The moment of closure using the proposed methodology is compared to the G-function approach, before and after correction of the pressure for temperature. Unlike physics-based techniques, the proposed method does not have any pre-assumption about the fracture's geometry or type of the well. The method solely relies on the pressure and temperature signals that are recorded during the injection and shut-in periods. Combining several analysis techniques to analyze DFIT (including the analysis of monitored temperature for a geothermal reservoir) is unique and maybe the first of its kind.

A breakthrough patent-pending pressure diagnostic technique using offset sealed wellbores as monitoring sources was introduced at the 2020 Hydraulic Fracturing Technology Conference. This technique quantifies various hydraulic fracture parameters using only a surface gauge mounted on the sealed wellbore(s). The initial concept, operational processes, and analysis techniques were developed and deployed by Devon Energy. By scaling and automating the process, Sealed Wellbore Pressure Monitoring (SWPM) is now available to the industry as a repeatable workflow that greatly reduces analysis time and improves visualizations to aid data interpretations. The authors successfully automated the SWPM analysis procedure using a cloud-based software platform designed to ingest, process, and analyze high-frequency hydraulic fracturing data. The minimum data for the analysis consists of the standard frac treatment data combined with the high-resolution pressure gauge data for each sealed wellbore. The team developed machine learning algorithms to identify the key events required by a sealed wellbore pressure analysis: the start, end, and magnitude of each pressure response detected in the sealed wellbore(s) while actively fracturing offset wells. The result is a rapid, repeatable SWPM analysis that minimizes individual interpretation biases. The primary deliverables from SWPM analyses are the Volumes to First Response (VFR) on a per stage basis. In many projects, multiple pressure responses within a single stage have been observed, which provides valuable insight into fracture network complexity and cluster/stage efficiency. Various methods are used to visualize and statistically analyze the data. A scalable process facilitates creating a statistical database for comparing completion designs that can be segmented by play, formation, or other geological variations. Completion designs can then be optimized based upon the observed well responses. With enough observations and based on certain spacings, probabilities of when to expect fracture interactions could be assigned for different plays.

In this case study, we apply a novel fracture imaging and interpretation workflow to take a systematic look at hydraulic fractures captured during thorugh fracture coring at the Hydraulic Fracturing Test Site (HFTS) in Midland Basin. Digital fracture maps rendered using high resolution 3D laser scans are analyzed for fracture morphology and roughness. Analysis of hydraulic fracture faces show that the roughness varies systematically in clusters with average cluster separation of approximately 20' along the core. While isolated smooth hydraulic fractures are observed in the dataset, very rough fractures are found to be accompanied by proximal smoother fractures. Roughness distribution also helps understand the effect of stresses on fracture distribution. Locally, fracture roughness seems to vary with fracture orientations indicating possible inter-fracture stress effects. At the scale of stage lengths however, we see evidence of inter-stage stress effects. We also observe fracture morphology being strongly driven by rock properties and changes in lithology. Identified proppant distribution along the cored interval is also correlated with roughness variations and we observe strong positive correlation between proppant concentrations and fracture roughness at the local scale. Finally, based on the observed distribution of hydraulic fracture properties, we propose a conceptual spatio-temporal model of fracture propagation which can help explain the hydraulic fracture roughness distribution and ties in other observations as well.

Water hammer is oscillatory pressure behavior in a wellbore resulting from the inertial effect of flowing fluid being subjected to an abrupt change in velocity. It is commonly observed at the end of large-scale hydraulic fracturing treatments after fluid injection rate is rapidly reduced or terminated. In this paper, factors affecting treatment-related water hammer behavior are disclosed, and field studies are introduced correlating water hammer characteristics to fracture intensity and well productivity. A simulator based on fundamental fluid-mechanics concepts was developed to model water hammer responses for various wellbore configurations and treatment characteristics. Insight from the modeling work was used to develop an optimal process of terminating fluid injection to obtain a consistent, identifiable oscillatory response for evaluating water hammer periodicity, decay rate, and oscillatory patterns. A completion database was engaged in a semi-automated process to evaluate numerous treatments. A data screening method was developed and implemented for enhancing interpretation reliability. Derived water hammer components were correlated to fracture intensity, well productivity and in certain cases, loss of treatment confinement to the intended treatment interval. Using the above process, thousands of hydraulic fracturing treatments were evaluated, and the results of that work are included in this study. The treatments were performed in wells based in Texas, South America, and Canada and completed in low permeability and unconventional reservoirs. The water hammer decay rate was determined to be a reliable indication of the system friction (friction in the wellbore and hydraulic fracture network) that drains energy from the water hammer pulse. In unconventional reservoirs characterized by small differences in the minimum and maximum horizontal stresses, high system friction correlated positively with fracture intensity/complexity and well performance. Results were constrained with instantaneous shut-in pressure (ISIP) and pressure falloff measurements to identify instances of direct communication with previously treated offset wellbores. The resulting analyses provided: – identification of enhanced-permeability intervals – indications of hydraulic fracture geometry – assessment of treatment modifications intended to enhance fracture complexity – identification of loss of treatment confinement to the intended interval – location of associated points of failure in the wellbore Topics covered in the paper include: Introduction Joukowsky Equation Period and Boundary Conditions Review of Prior Work on Water Hammer Analysis Shut-In Pressure Data, Analysis, and Model Data collection frequency Data issues and requirements Water Hammer Analytical Method Water Hammer Model Effects on Water hammer signature Fluid properties Step-down rate change and duration Perforation friction Applications Identification of Boundary Condition Identification of Treatment Stage Isolation Identification of Casing Failure Depth Identification of Excess Period (Excess Length) Case Study – Water Hammer Data in an Unconventional Reservoir Interpretation of frac geometry and friction in the fracture Relationship to well productivity

In multi-stage plug-and-perf horizontal well completions, there are a multitude of moving parts and variables to consider when evaluating performance drivers. Properly identifying performance drivers allows an operator to focus their efforts to maximize the rate of return of resource development. Typically, well-to-well comparisons are made to help identify performance drivers, but in many cases the differences are not clear. Identifying these drivers may require a better understanding of performance variability along a single lateral. Data analytics can help to identify performance drivers using existing data from development activities. In the case study below, multiple diagnostics are utilized to identify performance drivers. A combination of completion diagnostics including oil and water tracers, stimulation data, reservoir data, 3D seismic, and borehole image logs were collected on a set of wells in the early appraisal phase of a field. Using oil tracers as the best indication of stage level performance along the laterals, data analytics is applied to uncover the relationships between the tracers and the numerous diagnostics. After smoothing was applied to the dataset, trends between oil tracer recovery, several independent variables and features seen in image logs and 3D seismic were identified. All the analyses pointed to decreasing tracer recovery, and likely decreased oil production, near faulted areas along each lateral. A random forest model showed a moderate prediction power, where the model's predicted tracer recovery on blind stages was able to explain 54% of the variance seen in the tracer response (r 2 =0.54). This analysis suggests the identification of certain faulted areas along the wellbore could lead to ways of improving individual well economics by adjusting completion design in these areas.

Recovery factor for multi-fractured horizontal wells (MFHWs) at development spacing in tight reservoirs is closely related to the effective horizontal and vertical extents of the hydraulic fractures. Direct measurement of pressure depletion away from the existing producers can be used to estimate the extent of the hydraulic fractures. Monitoring wells equipped with downhole gauges, DFITs from multiple new wells close to an existing (parent) well, and calculation of formation pressure from drilling data are among the methods used for pressure depletion mapping. This study focuses on acquisition of pressure depletion data using multi-well diagnostic fracture injection tests (DFITs), analysis of the results using reservoir simulation, and integration of the results with production data analysis of the parent well using rate-transient analysis (RTA) and reservoir simulation. In this method, DFITs are run on all the new wells close to an existing (parent) well and the data is analyzed to estimate reservoir pressure at each DFIT location. A combination of the DFIT results provides a map of pressure depletion around the existing well, while production data analysis of the parent well provides fracture conductivity and surface area and formation permeability. Furthermore, reservoir simulation is tuned such that it can also match the pressure depletion map by adjusting the system permeability and fracture geometry of the parent well. The workflow of this study was applied to two field case from Montney formation in Western Canadian Sedimentary Basin. In Field Case 1, DFIT results from nine new wells were used to map the pressure depletion away from the toe fracture of a parent well (four wells toeing toward the parent well and five wells in the same direction as the parent). RTA and reservoir simulation are used to analyze the production data of the parent well qualitatively and quantitatively. The reservoir model is then used to match the pressure depletion map and the production data of the parent well and the outputs of the model includes hydraulic fracture half-lengths on both sides of the parent well, formation permeability, fracture surface area and fracture conductivity. In Field Case 2, the production data from an existing well and DFIT result from a new well toeing toward the existing wells were incorporated into a reservoir simulation model. The model outputs include system permeability and fracture surface area. It is recommended to try the method for more cases in a specific reservoir area to get a statistical understanding of the system permeability and fracture geometry for different completion designs. This study provides a practical and cost-effective approach for pressure depletion mapping using multi-well DFITs and the analysis of the resulting data using reservoir simulation and RTA. The study also encourages the practitioners to take every opportunity to run DFITs and gather pressure data from as many well as possible with focus on child wells.

The Walloons coal measures located in Surat Basin (eastern Australia) is a well-known coal seam gas play that has been under production for several years. The well completion in this play is primarily driven by coal permeability which varies from 1 Darcy or more in regions with significant natural fractures to less than 1md in areas with underdeveloped cleat networks. For an economic development of the latter, fracturing treatment designs that effectively stimulate numerous and often thin coals seams, and enhance inter-seam connectivity, are a clear choice. Fracture stimulation of Surat basin coals however has its own challenges given their unique geologic and geomechanical features that include (a) low net to gross ratio of ~0.1 in nearly 300 m (984.3 ft) of gross interval, (b) on average 60 seams per well ranging from 0.4 m to 3 m in thickness, (c) non-gas bearing and reactive interburden, and (d) stress regimes that vary as a function of depth. To address these challenges, low rate, low viscosity, and high proppant concentration coiled tubing (CT) conveyed pinpoint stimulation methods were introduced basin-wide after successful technology pilots in 2015 ( Pandey and Flottmann 2015 ). This novel stimulation technique led to noticeable improvements in the well performance, but also highlighted the areas that could be improved – especially stage spacing and standoff, perforation strategy, and number of stages, all aimed at maximizing coal coverage during well stimulation. This paper summarizes the findings from a 6-well multi-stage stimulation pilot aimed at studying fracture geometries to improve standoff efficiency and maximizing coal connectivity amongst various coal seams of Walloons coal package. In the design matrix that targeted shallow (300 to 600 m) gas-bearing coal seams, the stimulation treatments varied in volume, injection rate, proppant concentration, fluid type, perforation spacing, and standoff between adjacent stages. Treatment designs were simulated using a field-data calibrated, log-based stress model. After necessary adjustments in the field, the treatments were pumped down the CT at injection rates ranging from 12 to 16 bbl/min (0.032 to 0.042 m 3 /s). Post-stimulation modeling and history-matching using numerical simulators showed the dependence of fracture growth not only on pumping parameters, but also on depth. Shallower stages showed a strong propensity of limited growth which was corroborated by additional field measurements and previous work in the field ( Kirk-Burnnand et al. 2015 ). These and other such observations led to revision of early guidelines on standoff and was considered a major step that now enabled a cost-effective inclusion of additional coal seams in the stimulation program. The learnings from the pilot study were implemented on development wells and can potentially also serve as a template for similar pinpoint completions worldwide.

Predicting fracture behavior is important for well placement design and for optimizing multi-well development production. This requires the use of fracturing models that are calibrated to represent field measurements. However, because hydraulic fracture models include complex physics and uncertainties and have many variables defining these, the problem of calibrating modeling results with field responses is ill-posed. There are more model variables than can be changed than field observations to constrain these. It is always possible to find a calibrated model that reproduces the field data. However, the model is not unique and multiple matching solutions exist. The objective and scope of this work is to define a workflow for constraining these solutions and obtaining a more representative model for forecasting and optimization. We used field data from a multi-pad project in the Delaware play, with actual pump schedules, frac sequence, and time delays as used in the field, for all stages and all wells. We constructed a hydraulic fracturing model using high-confidence rock properties data and calibrated the model to field stimulation treatment data varying the two model variables with highest uncertainty: tectonic strain and average leak-off coefficient, while keeping all other model variables fixed. By reducing the number of adjusting model variables for calibration, we significantly lower the potential for over-fitting. Using an ultra-fast hydraulic fracturing simulator, we solved a global optimization problem to minimize the mismatch between the ISIPs and treatment pressures measured in the field and simulated by the model, for all the stages and all wells. This workflow helps us match the dominant ISIP trends in the field data and delivers higher confidence predictions in the regional stress. However, the uncertainty in the fracture geometry is still large. We also compared these results with traditional workflows that rely on selecting representative stages for calibration to field data. Results show that our workflow defines a better global optimum that best represents the behavior of all stages on all wells, and allows us to provide higher-confidence predictions of fracturing results for subsequent pads. We then used this higher confidence model to conduct sensitivity analysis for improving the well placement in subsequent pads and compared the results of the model predictions with the actual pad results.

The recent seismicity rate increase in Fox Creek is believed to be linked to the hydraulic fracturing operations near the region. However, the spatiotemporal evolution of hydraulic fracturing-induced seismicity is not well understood. Here, a coupled approach of geology, geomechanics, and hydrology is proposed to characterize the spatiotemporal evolution of hydraulic fracturing-induced seismicity. The seismogenic faults in the vicinity of stimulated wells are derived from the focal mechanisms of mainshock event and lineament features of induced events. In addition, the propagation of hydraulic fractures is simulated by using the PKN model, in combination with inferred fault, to characterize the possible well-fault hydrological communication. The original stress state of inferred fault is determined based on the geomechanics analysis. Based on the poroelasticity theory, the coupled flow-geomechanics simulation is finally conducted to quantitatively understand the fluid diffusion and poroelastic stress perturbation in response to hydraulic fracturing. A case study of a moment-magnitude-3.4 earthquake near Fox Creek is utilized to demonstrate the applicability of the coupled approach. It is shown that hydraulic fractures propagated along NE45° and connected with one North-south trending fault, causing the activation of fault and triggered the large magnitude event during fracturing operations. The barrier property of inferred fault under the strike-slip faulting regime constrains the nucleation position of induced seismicity within the injection layer. The combined changes of pore pressure and poroelastic stress caused the inferred fault to move towards the failure state and triggered the earthquake swarms. The associated spatiotemporal changes of Coulomb Failure Stress along the fault plane is well in line with the spatiotemporal pattern of induced seismicity in the studied case. Risks of seismic hazards could be reduced by decreasing fracturing job size during fracturing stimulations.

Well spacing and stimulation design are amongst the highest impact design variables which can dictate the economics of an unconventional development. The objective of this paper is to showcase a numerical simulation workflow, with emphasis on the hydraulic fracture simulation methodology, which optimizes well spacing and completion design simultaneously. The workflow is deployed using Cloud Computing functionality, a step-change over past simulation methods. Workflow showcased in this paper covers the whole cycle of 1) petrophysical and geomechanical modeling, 2) hydraulic fracture simulations and 3) reservoir simulation modeling, followed by 4) design optimization using advanced non-linear methods. The focus of this paper is to discuss the hydraulic fracture simulation methods which are an integral part of this workflow. The workflow is deployed on a dataset from a multi-well pad completed in late 2018 targeting two landing zones in the Vaca Muerta shale play. On calibrated petrophysical and geomechanical model, hydraulic fracture simulations are conducted to map the stimulated rock around the wellbores. Finely gridded base model is utilized to capture the property variation between layers to estimate fracture height. The 3d discrete fracture network (DFN) built for the acreage is utilized to pick the natural fracture characteristics of the layers intersected by the wellbores. The methodology highlights advances over the past modeling approaches by including the variation of discrete fracture network between layers. The hydraulic fracture model in conjunction with reservoir flow simulation is used for history matching the production data. On the history matched model, a design of experiments (DOE) simulation study is conducted to quantify the impact of a wide range of well spacing and stimulation design variables. These simulations are facilitated by the recent deployments of cloud computing. Cloud computing allows parallel running of hundreds of hydraulic fracturing and reservoir simulations, thereby allowing testing of many combinations of stimulation deigns and well spacing and reducing the effective run time from 3 months on a local machine to 1 week on the cloud. Output from the parallel simulations are fitted with a proxy model to finally select the well spacing and stimulation design variables that offer the minimum unit development cost i.e. capital cost-$ per EUR-bbl. The workflow illustrates that stimulation design and well spacing are interlinked to each other and need to be optimized simultaneously to maximize the economics of an unconventional asset. Using the workflow, the team identified development designs which increase EUR of a development area by 50-100% and reduce the unit development cost ($/bbl-EUR) by 10-30%.

In the last decade, we have observed major advancements in different modeling techniques for hydraulic fracturing propagation. Direct monitoring techniques such as fibre-optics can be used to calibrate these models and significantly enhance our understanding of subsurface processes. In this study, we present field monitoring observations indicating consistently oriented, planar fractures in an offset-well at different landing zones in the Permian basin. Frac hit counts, location, and timing statistics can be compiled from the data using offset wells at different distances and depths. The statistics can be used to calibrate a detailed three-dimensional fully coupled hydraulic fracturing and reservoir simulator. In addition to these high-level observations, detailed fibre signatures such as strain response during frac arrival to the monitoring well, post shut-in frac propagation and frac speed degradation with length can be modeled using the simulator for further calibration purposes. Application to frac modeling calibration is presented through different case studies. The simulator was used to directly generate the ‘waterfall plot’ output from the fibre-optic under a variety of scenarios. The history match to the large, detailed synthetic fibre dataset provided exceptional model calibration, enabling a detailed description of the fracture geometry, and a high-confidence estimation of key model parameters. The detailed synthetic fibre data generated by the simulator were remarkably consistent with the actual data. This indicates a good consistency with classical analytical fracture mechanics predictions and further confirm the interpretation of planar fracture propagation. This study shows how careful integration of offset-well fibre-optic measurements can provide detailed characterization of fracture geometry, growth rate, and physics. The result is a detailed picture of hydraulic fracture propagation in the Midland Basin. The comparison of the waterfall plot simulations and data indicate that hydraulic fractures can, in fact, be very well modeled as nearly-linear cracks (the ‘planar fracture modeling’ approach).

The goal of this work is to calibrate a regional predictive model for maximum magnitude of seismic activity associated with hydraulic-fracturing in low-permeability formations in the Western Canada Sedimentary Basin (WCSB). Hydraulic fracturing data (i.e. total injected volume, injection rate, and pressure) were compiled from more than 40,000 hydraulic-fractured wells in the WCSB. These wells were drilled into more than 100 different formations over a 20-year period (January 1st, 2000 and January 1st, 2020). The total injected volume per unit area was calculated utilizing an area of 0.2° in longitude by 0.1° in latitude (or approximately 13x11km, somewhat larger than a standard township of 6x6 miles). This volume was then used to correlate with reported seismicity in the same unit areas. Collectively, within the 143 km 2 area considered in this study, a correlation between the total injected volume and the maximum magnitude of seismic events was observed. Results are similar to the maximum-magnitude forecasting model proposed by A. McGarr (JGR, 2014) for seismic events induced by wastewater injection wells in central US. The McGarr method is also based on the total injected fluid per well (or per multiple nearby wells located in the same unit area). However, in some areas in the WCSB, lower injected fluid volumes than the McGarr model predicts were needed to induce seismic events of magnitude 3.0 or higher, although with a similar linear relation. The result of this work is the calculation of a calibration parameter for the McGarr model to better predict the magnitudes of seismic events associated with the injected volumes of hydraulic fracturing. This model can be used to predict induced seismicity in future unconventional hydraulic fracturing treatments and prevent large-magnitude seismic events from occurring. The rich dataset available from the WCSB allowed us to carry out a robust analysis of the influence of critical parameters (such as the total injected fluid) in the maximum magnitude of seismic events associated with the hydraulic-fracturing stimulation of unconventional wells. This analysis could be replicated for any other sedimentary basin with unconventional wells by compiling similar stimulation and earthquake data as in this study.

Pressure and stage volume are the least expensive and most readily available data for diagnostic analysis of hydraulic fracturing operations. Case history data from the Midland Basin is used to demonstrate how high-quality, time-synchronized pressure measurements at a treatment and an offsetting shut-in producing well can provide the necessary input to calculate fracture geometries at both wells and estimate perforation cluster efficiency at the treatment well. No special wellbore monitoring equipment is required. In summary, the methods outlined in this paper quantifies fracture geometries as compared to the more general observations of Daneshy (2020) and Haustveit et al. (2020) . Pressures collected in Diagnostic Fracture Injection Tests (DFITs), select toe-stage full-scale fracture treatments, and offset observation wells are used to demonstrate a simple workflow. The pressure data combined with Volume to First Response ( Vfr ) at the observation well is used to create a geometry model of fracture length, width, and height estimates at the treatment well as illustrated in Figure 1 . The producing fracture length of the observation well is also determined. Pressure Transient Analysis (PTA) techniques, a Perkins-Kern-Nordgren (PKN) fracture propagation model and offset well Fracture Driven Interaction (FDI) pressures are used to quantify hydraulic fracture dimensions. The PTA-derived Farfield Fracture Extension Pressure, FFEP , concept was introduced in Nicholson et al. (2019) and is summarized in Appendix B of this paper. FFEP replaces Instantaneous Shut-In Pressure, ISIP , for use in net pressure calculations. FFEP is determined and utilized in both DFITs and full-scale fracture inter-stage fall-off data. The use of the Primary Pressure Derivative (PPD) to accurately identify FFEP simplifies and speeds up the analysis, allowing for real time treatment decisions. This new technique is called Rapid-PTA. Additionally, the plotted shape and gradient of the observation-well pressure response can identify whether FDI's are hydraulic or poroelastic before a fracture stage is completed and may be used to change stage volume on the fly. Figure 1 Fracture Geometry Model with FDI Pressure Matching Case studies are presented showing the full workflow required to generate the fracture geometry model. The component inputs for the model are presented including a toe-stage DFIT, inter-stage pressure fall-off, and the FDI pressure build-up. We discuss how to optimize these hydraulic fractures in hindsight (look-back) and what might have been done in real time during the completion operations given this workflow and field-ready advanced data-handling capability. Hydraulic fracturing operations can be optimized in real time using new Rapid-PTA techniques for high quality pressure data collected on treating and observation wells. This process opens the door for more advanced geometry modeling and for rapid design changes to save costs and improve well productivity and ultimate recovery.

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.