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Nicholson, A. Kirby (Pressure Diagnostics Ltd.) | Bachman, Robert C. (Pressure Diagnostics Ltd.) | Scherz, R. Yvonne (Endeavor Energy Resources) | Hawkes, Robert V. (Cordax Evaluation Technologies Inc.)
Abstract 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.
Liu, Yongzan (Texas A&M University) | Wu, Kan (Texas A&M University) | Jin, Ge (Colorado School of Mines) | Moridis, George (Texas A&M University and Lawrence Berkeley National Laboratory) | Kerr, Erich (SM Energy) | Scofield, Reid (SM Energy) | Johnson, Andrew (SM Energy)
Summary Low-frequency distributed acoustic sensing (LF-DAS) data are a powerful attribute to detect fracture hits and characterize fracture geometry during multistage hydraulic fracturing treatments in unconventional reservoirs. The DAS data in low-frequency bands linearly correlate with strain and strain rate induced by dynamic fracture propagation. Due to the complexity of multiple-fracture propagation in unconventional reservoirs, the measured signals from different wells exhibit various characteristics. Mechanisms causing the differences are not well understood, which makes the interpretation of real LF-DAS data and detection of fracture hits very challenging. Hence, it is necessary to relate the observed strain/strain-rate signatures to specific fracture patterns based on the physical model of rock deformation during fracture propagation and to quantitatively characterize signatures surrounding fracture hits. In this study, we have applied our in-house fracture propagation model to simulate simultaneous multiple-fracture propagation as well as fracture-induced strain and strain-rate responses along an offset monitor well. Then a general guideline for fracture-hit detection is proposed based on quantitative analysis of strain/strain-rate responses during multiple-fracture propagation. Finally, a set of field examples are presented to demonstrate the potential of LF-DAS data on hydraulic fracturing monitoring. During multiple-fracture propagation, a “heart-shaped” zone with positive strain rates may be identified for each fracture before the fracture hit. Immediately after the fracture encounters the monitor well, part of the fiber within the fracture path keeps being extended, while the fiber sections off the path become compressed. Three 1D features along the channel (location) axis are designed to detect fracture hits. The features are maximum strain rate, the summation of strain rates, and summation of strain-rate amplitudes. Channels with fracture hits usually exhibit significant peak values of the three features. However, the characteristic signatures can be less detectable when the gauge length is close to the cluster spacing. Connections between fracture-hit locations and cluster perforations clearly reflect the fracture propagation direction. The field examples illustrate the complexity of real LF-DAS signals and demonstrate the adequacy of the proposed guideline for fracture-hit detection with multicluster completion. The fractures propagate nearly perpendicular to the horizontal wellbore in this unconventional shale formation. In addition, four to five fractures out of eight perforation clusters can propagate 396.24 m (1,300 ft) and hit the monitor well, and the “heel-biased” fracture pattern is observed (fractures that do not hit the monitor well are usually close to the toe side). Fracturing fluid leaking off into the previous stage can also be diagnosed, which could negatively affect the completion efficiency.
This paper presents a case study of fracture interaction mitigation in a multistage horizontal stimulation of an offshore Black Sea well. A multi-faceted approach in applying lessons learned and pre-job geo-mechanical analysis of depletion-induced stress differential and its effects on fracture interactions will be discussed. Details of on-the-job, real-time bottom-hole pressure monitoring of nearby wells, with the effort of on-the-fly pumping schedule changes, will also be provided.
An analysis was conducted on past fracture interactions observed from multistage stimulation jobs in the area. Depletion, distances between producing wells, and a stress analysis was performed using fracture simulation software, and a consequent analysis of fracture geometry was applied. A bottom-hole gauge pressure profile assessment of nearby wells, including the pre-stimulation, shut-in, and post-stimulation period of the targeted well, was completed. A redesigned treatment was applied, considering a mitigation plan for potential on-the-fly changes during pumping. A holistic tracer analysis of production contribution between stages and wells was performed, with the goal of understanding possible crossflow of production fluids.
Past-fracture interaction events have been analyzed, and clear drivers for fracture hit communication were observed. Extreme depletion effects were a primary factor in enabling fracture communication. The preferential fracture growth was further enabled owing to the continuous production of nearby wells and no shut-in implementation. The 3D geo-mechanical model was built using pertinent data from the targeted and nearby wells. The model was further optimized using fracture geometry outputs, and constraints were input to limit the fracture growth and avoid communication. The outcome of the analysis showed a clear driving force behind the interactions was depletion. An on-the-job assessment of diagnostic tests yielded a heterogeneous behavior of the horizontal segment, further proving stress differentials along the lateral. An overall chemical tracer analysis of the targeted and nearby wells was completed using pre- and post-stimulation fluid samples. The results were crucial in understanding the stimulation approach and possible crossflow effects due to fracture communication. Additionally, using bottom-hole temperature readings, a rudimentary cool-down and heat-back analysis was performed to better understand possible fluid interactions with nearby wells and optimize fluid design.
Intra-stage fracture interference presents unique events and challenges that are typically managed on a case-by-case basis, and this work presents the critical analyses that are paramount to planning stimulation treatments in highly depleted segments and reservoirs with wells in close proximity.
Extensive literature has been published concerning Fracture Driven Interactions (FDIs). Many of these works describe FDI anatomy, physics and impact on existing wells including the success or failure of various mitigation techniques. In this paper, time synchronized FDI surface pressure data from existing offsetting wells is used to study fracture wing growth. This includes timing, fracture half-length estimation and observing the movement of injected fluid sequentially from well-to-well during fracturing operations.
In 2019, an in-fill Eagle Ford well development consisting of two newly drilled wells and seven offsetting existing wells was performed. The new wells were located such that each treatment well was bounded on both sides by existing wells. Further, there were first order wells (closest well to treatment well) and second order wells (next well over from the first order well) bounding one of the treatment wells and FDIs generated pressure communication were seen in both the first order well and the second order well. Six of the existing wells were preloaded. This was done to test the efficacy of preloading to protect the existing wells from FDI damage.
The workflow was organized into first deriving detailed data from all FDIs that occurred in individually monitored wells. This was followed by examining well pairs bounding the two treatment wells to study fracture wing development in each stage. Lastly, first order and second order wells that had sequential FDIs from the treatment wells were examined to study fluid volumes and timing between wells as well as FDI magnitude dampening in second order wells. All monitored wells were left shut-in during the completion operations. The wireless surface pressure monitoring sensors were time-synchronized to internet time and the data was viewed in real-time.
The authors’ found that preloading dampened FDIs but did not completely stop them. The degree of the success of preloading was graded by determining a reduction of recovery time (if any) and how well production rates were protected. At the time of this writing, flowback operations are ongoing and one primary well recovered to pre-frac rates within one week. We anticipate a corresponding reduction of recovery time in the other wells.
This paper presents methods and processes that offer a solution to identify candidate stages for FDI mitigation and potential optimization of project economics. The time synchronization with internet time eliminated all uncertainty with regards to timing. As will be shown, for this type of study, there cannot be uncertainty with timing. No inconsistencies in timing were found. It was also determined that FDI data must be viewable in real-time for it to be used to make on-the-fly mitigation decisions. In this project, a "passive well defense" technique was utilized; preload but take no other actions.
During fracturing, pressure responses are often observed in a nearby offset monitor well as hydraulic fractures propagate from the treatment well towards the monitor well. These pressure responses can be caused by, (a) purely poroelastic interactions between the treatment and monitor well fractures, (b) a combination of poroelastic interaction and hydraulic connection between the fractures (mixed response) or (c) massive direct frac-hits from the treatment into the monitor well fractures. In this work, we demonstrate an automated pattern recognition workflow to systematically identify and interpret the different types of pressure responses observed in field data from the Permian Basin.
An automated pattern recognition workflow based on Python scripting has been developed that parses field offset well pressure data during fracturing from multiple wells, stage-by-stage, in each well. The script develops overlay-plots containing treatment and monitor well pressure for each stage, which can be stored in a directory of the user’s choice (for future reference). The script then automatically determines the magnitude of pressure response as well as the type of pressure interference - "purely-poroelastic", "mixed" [poroelastic + hydraulic] or "direct frac-hit" - and the output is automatically stored stage-by-stage in a user-friendly text delimited (".txt", ".csv" or ".xlsx") format while the script executes. In addition, the script can also calculate the fracture azimuth based on relative distance between interacting stages and the magnitude of the observed pressure response.
In case of a purely poroelastic response, pressure fall-off is observed in the monitor well as soon as the nearby treatment well is shut-in (Seth et al., 2019a). This is an important distinction between purely poroelastic responses and other types of pressure responses where a pressure increase is observed even after the nearby treatment well is shut-in. The magnitude of pressure response also varies with the type of pressure response. Typically, purely poroelastic pressure responses range between 1-100 psi (sometimes higher) depending upon the distance and overlap between the interacting fractures, whereas mixed pressure responses range between 10s-100s of psi. Direct frac-hits usually cause a massive increase in the offset monitor well pressure (100s-1000s of psi) and are relatively easy to spot visually as they disrupt the pressure response trend.
It is crucial to correctly identify and interpret the type of pressure interference observed in field offset well pressure data before using this data for further analysis (such as fracture geometry estimation). This work details the different types of pressure responses typically observed in field data and provides guidelines on identifying and characterizing these responses correctly. In addition, the demonstrated automated workflow introduces a novel tool to systematically parse and characterize field offset well pressure data efficiently and calculate fracture azimuth based on magnitude of observed pressure response and distance between the interacting stages.
Liu, Yongzan (Texas A&M University) | Wu, Kan (Texas A&M University) | Jin, Ge (Colorado School of Mines) | Moridis, George J. (Texas A&M University / Lawrence Berkeley National Laboratory) | Kerr, Erich (SM Energy) | Scofield, Reid (SM Energy) | Johnson, Andrew (SM Energy)
Low-frequency distributed acoustic sensing (LF-DAS) data is a powerful attribute to detect fracture hits and characterize fracture geometry during multi-stage hydraulic fracturing treatments in unconventional reservoirs, which can afford operators the opportunity to improve on completion design decisions. The DAS data in low-frequency bands linearly correlate with strain and strain rate induced by dynamic fracture propagation. Due to the complexity of multiple-fracture propagation in unconventional reservoirs, the measured signals from different wells exhibit various characteristics and mechanisms causing the differences to not be well understood, which makes the interpretation of real LF-DAS data and detection of fracture hits much challenging. Hence, it is necessary to relate the observed strain/strain-rate signatures to specific fracture patterns based on the physical model of rock deformation during fracture propagation and to quantitatively characterize signatures surrounding fracture hits. In this study, we have applied our in-house fracture propagation model to simulate simultaneous multiple-fracture propagation as well as fracture-induced strain and strain-rate responses along an offset monitor well, which are monitored in a real-time manner. Then a general guideline for fracture-hit detection is proposed based on quantitative analysis of strain/strain-rate responses during multiple-fracture propagation. Finally, a set of field examples are presented to demonstrate the potential of LF-DAS data on hydraulic fracturing monitoring.
During multiple-fracture propagation, a "heart-shape" zone with positive strain rates may be identified for each fracture before fracture hit. Immediately after the fracture encounters the monitor well, part of the fiber within the fracture path keeps being extended, while the fiber sections off the path become compressed. Three 1D features along the channel (location) axis, which are maximum strain rate, the summation of strain rates, and summation of strain-rate amplitudes, are designed to detect fracture hits. Channels with fracture hits usually exhibit significant peak values of the three features. However, the characteristic signatures can be less detectable when the gauge length is close to the cluster spacing. Connections between fracture-hit locations and cluster perforations clearly reflect the fracture propagation direction. The field examples illustrate the complexity of real LF-DAS signals and demonstrate the adequacy of the proposed guideline for fracture-hit detection with multi-cluster completion. The fractures propagate nearly perpendicular to the horizontal wellbore in this unconventional shale formation. In addition, 4-5 fractures out of 8 perforation clusters can propagate 1300 ft and hit the monitor well, and the ‘heel-biased’ fracture pattern is observed (fractures that do not hit the monitor well are usually close to the toe side). Fracturing fluid leaking off into the previous stage can also be diagnosed, which could negatively affect the completion efficiency.
This Permian Basin case study investigates the impact of primary (i.e., "parent") well depletion on infill (i.e., "child") well completions. The operator used electromagnetic frac fluid tracking to monitor the hydraulic fracturing treatments of three infill wells. The project’s primary objectives centered on characterizing fracture-driven interference and understanding the impact of depletion on the new completions. Additionally, data were used to define fracture azimuths to help inform well spacing to improve development throughout the asset. A multidisciplinary team compared the frac fluid tracking results with offset parent well pressure data to better characterize the dynamic interaction between the treatment, geology and depletion caused by the primary wells.
The three treatment wells, which targeted two different source rocks, were sandwiched between two producing parent wells to the east and three producing parent wells to the west. It was hypothesized that the five primary wells would likely create a large depleted reservoir volume surrounding them that would affect the hydraulic stimulations of the infill wells. To achieve the project objectives, the operator used electromagnetic (EM) frac fluid tracking combined with pressure data from two of the producing primary wells, one on either side (east and west). EM fluid tracking uses Controlled Source Electromagnetics (CSEM) technology to measure the changes in subsurface resistivity caused by treatment fluid injection. Injecting fracturing fluid into the source rock alters a generated EM field causing measurable differences from the surface and allows engineers to map the frac fluid movement over time. This technique involves a concert of disciplines from the electrical engineers that developed EM fluid tracking, to the geophysicists that process the results and interpret them with completion and reservoir engineers. The results can indicate when, where from, and to what extent the treatment fluid moves toward primary wells and depleted areas.
The authors are experienced in monitoring infill well completions and observing fracture-driven interactions with primary wells. While the interpretation will be ongoing, the EM fluid tracking proved to be a valuable tool for observing fracture development and understanding reservoir dynamics, particularly in regard to the interaction of the hydraulic stimulations from the infill wells with pad-scale faulting and depletion caused by the parent wells.
Abstract In this case study, three sequential well pads were designed, stimulated and monitored to evaluate 1. Treatment order of stacked wells across multiple benches, 2. Completions optimization in proximity to a parent well and 3. The efficacy of treatment sequence in proximity to parent wells. Microseismic data were evaluated in conjunction with tracer and pressure data to provide a more detailed understanding of reservoir deformation and well connectivity using statistical approaches that consider the collective behavior of seismicity. High-resolution microseismic involves analyzing spatio-temporal trends in seismicity rather than reliance on microseismic event clouds to provide more meaningful assessment of hydraulically-linked seismicity vs. stress-driven seismicity. The findings of the first two case studies were applied to the stimulation of the third well pad to demonstrate the role of well sequencing in proximity to depleted zones and the impacts of completions design in managing well communication. Here we discuss the benefit of high-resolution microseismic in assessing perceived well interference by delineating the difference between hydraulically-linked and stress-driven seismicity recorded during multi-well hydraulic fracturing programs. In applying knowledge of reservoir deformation processes to customize stimulation programs, operators have additional tools to help manage reservoir stress, limit unwanted well communication and optimize production.
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).
Morales, Adrian (Chesapeake Energy Corp.) | Holman, Robert (Chesapeake Energy Corp.) | Nugent, Drew (Chesapeake Energy Corp.) | Wang, Jingjing (Chesapeake Energy Corp.) | Reece, Zach (Chesapeake Energy Corp.) | Madubuike, Chinomso (Chesapeake Energy Corp.) | Flores, Santiago (Chesapeake Energy Corp.) | Berndt, Tyson (Chesapeake Energy Corp.) | Nowaczewski, Vincent (Chesapeake Energy Corp.) | Cook, Stephanie (Chesapeake Energy Corp.) | Trumbo, Amanda (Chesapeake Energy Corp.) | Keng, Rachel (Chesapeake Energy Corp.) | Vallejo, Julieta (Chesapeake Energy Corp.) | Richard, Rex (Chesapeake Energy Corp.)
Abstract An integrated project can take many forms depending on available data. As simple as a horizontally isotropic model with estimated hydraulic fracture geometries used for simple approximations, to a large scale seismic to simulation workflow. Presented is a large-scale workflow designed to take into consideration a vast source of data. In this study, the team investigates a development area in the Eagle Ford rich in data acquisition. We develop a robust workflow, taking into account field data acquisition (seismic, 4D seismic and chemical tracers), laboratory (geomechanical, geochemistry and PVT) measurements and correlations, petrophysical measurements (characterization, facies, electrical borehole image), real time field surveillance (microseismic, MTI, fracture hit prevention and mitigation program through pressure monitoring) and finally integrating all the components of a complex large scale project into a common simulation platform (seismic, geomodelling, hydraulic fracturing and reservoir simulation) which is used to run sensitivities. The workflow developed and applied for this project can be scaled for projects of any size depending on the data available. After integrating data from various disciplines, the following primary drivers and reservoir understanding can be concluded. At a given oil price, optimum well spacing for a given completion strategy can be developed to maximize rate of return of the project. Many operators function in isolated teams with a genuine effort for collaboration, however genuine effort is not enough for a successful integrated modelling project, a dedicated multidisciplinary team is required. We present what is to our knowledge, one of the most complete data sets used for an integrated modelling project to be presented to the public. The specific lessons from the project are applied to future Eagle Ford projects, while the overall workflow developed can be tailored and applied to any future field developments.