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Bommer, Pete (Abraxas Petroleum Corporation) | Iriarte, Jessica (Well Data Labs) | Bayne, Marc (Abraxas Petroleum Corporation) | Cline, Colby (Abraxas Petroleum Corporation) | Ramirez, Alberto (Well Data Labs) | Van Domelen, Mary (Well Data Labs)
Fracture-driven interactions in horizontal wells are receiving considerable attention due to the costly negative effects on the full field development of unconventional reservoirs. Operators have tested various methods to minimize these interactions. Active well defense (AWD) describes the process of pumping into existing wells while completing new wells in a unit. Detailed evaluations of active well defense projects are cumbersome due to the extremely large volume of data generated from multiple sources, further complicated by the fact that the data are often stored in variable formats. This paper demonstrates that near real-time evaluation of an active well defense project is possible.
Minimization of fracture-driven interactions has been accomplished by a two-fold approach: optimization of the completion design for the new wells and improvements to the active defense process. Building upon previous successful projects (
Increasing the number of fracture initiation points along the lateral by maximizing perforation cluster efficiency is the first step toward minimizing fracture-driven interactions. A common tool is the use of dynamic diversion. Operators apply various diversion techniques in multi-stage fracturing to increase cluster efficiency. The ability to assess diverter performance in time-series data is valuable when optimizing fracture operations.
Active well defense is the second step in minimizing fracture-driven interactions (FDI). In this case study legacy wells are defended by pumping treated water into the legacy wells while completing new wells in the unit. FDIs are monitored with high resolution gauges while the pump-in rates into the legacy wells are dynamically adjusted based upon the pressure responses. Post-project evaluations involve multiple time-series data streams containing an extremely large amount of data. The raw data (.CSV files) are collected and analyzed using a cloud-based application optimized for time-series frac data. Combining the frac treatment data from the new well with the legacy well defense data and applying advanced analytics techniques, it is possible to identify trends and quantify the effectiveness of the active well defense process. Finally, well records and historical production data are combined with the treatment data to demonstrate the overall economic benefit of the process.
The main goal of hydraulic fracture treatments is to achieve a target geometry and conductivity within given operational and budget constraints. Evaluating past frac designs can help one to understand completion performance by highlighting and summarizing relevant engineering considerations. For example, to simulate fracture growth, the model requires a pumping schedule as an input. This study describes an automated procedure that identifies the different steps of a pumping schedule and generates several pertinent statistics based on the hydraulic fracturing time-series data collected in the field. Essentially, it returns an actual or "as pumped" fracture treatment schedule, which may differ from the designed one.
The dataset analyzed in this study includes the slurry rates and proppant concentrations for 577 stages from all major North American basins. The algorithms were calibrated using 112 stages and tested on the remaining 465 stages. The procedure first identifies the start and end times of various sections of the pump schedule (pad, acid pad, slurry, sweeps, and flush) by smoothing, normalizing, and then "rounding" the proppant concentration signal. The procedure then isolates sustained intervals with positive proppant concentration to identify acid pads and proppant stages. The remaining time intervals are associated with pads, sweeps, and flushes. Various statistics such as volumes, durations, and averages are computed for each interval.
Each slurry interval (proppant ramp) is then further broken down into its proppant steps. This is accomplished using quantization ideas from digital signal processing. Quantizing the signal maps the observed proppant concentrations in each step (noisy signal) to a representative value for that step. If the stage is pumped as planned, then these representative values should be close to the designed concentrations. The authors have two techniques to accomplish this: the first is based on clustering, and the second uses a piecewise constant regression based on recursive partitioning (decision-tree regression). The representative values are used to identify the proppant steps, and once the steps are identified, the process generates statistics for each step.
Hydraulic fracturing time-series data is an ideal target for analysis using signal processing techniques. The process correctly recognizes the start and end times of the various pumping steps: 95% of the picks (target events) identified by the procedure are within three seconds of the manual picks. This automation significantly reduces the time required for picks, allows fast auditing of existing picks, and provides an efficient method for analyzing historic pumping schedule data that is available only as PDF files. This is the first paper that describes a computational method to automatically extract a pumping schedule from hydraulic fracturing time-series data. The method uses techniques from digital signal processing and is accurate, robust, transparent, and fast.
Abstract This paper explores a holistic approach to characterize trouble stages by applying automated event recognition of abnormal pressure increases and associating those events to formation and operational causes. This analysis of pressure increases provides insight into the potential causes of operational difficulties, and the related diagnostics can suggest improvements to future pump schedules. Improving how stages are pumped is profitable both in the short-term (reducing wasted fluid and chemicals, and other remediation measures) and in the long-term (increased well productivity). Quantifying how design decisions ultimately affect operations can help decrease the frequency of operational problems and help realize these gains. In this study, the identification of problematic frac stages was initially performed manually (stage-by-stage) using a cloud-based hydraulic fracture data application. During this process, the team recognized that the problem stages had their own characteristic pressure signature - a sudden unexplained pressure increase in the absence of rate changes. A machine learning algorithm was then developed to automatically identify this type of signature. Additionally, previously published machine learning algorithms were used to recognize other operational events of interest, e.g., when proppant reaches the perforations. Then by combining the various events and creating short search windows around each abnormal pressure increase, it is possible to find concurrent operations that may be associated with the observed pressure behavior. A subsequent statistical analysis revealed that abnormal pressure increases often coincided with changes in proppant concentration in problem stages (stages with abnormal treating pressure behavior). This behavior may be due to near-wellbore effects caused by the changing fluid flow dynamics. Furthermore, it was observed that treating pressures that behaved contrary to hydrostatic pressure effects may be useful in identifying when injectivity is lost and provide an early signal for screen outs. Through this holistic approach, we were able to identify trouble stages and discern some diagnostics for automated detection of abnormal treating pressure increases. The team was able to identify areas within the stages that were inefficiently pumped, resulting in cost-savings through optimization of proppant and friction reducer (FR) loadings while maintaining a level of caution to prevent screen outs. Finally, the automated detection of pressure anomalies offers a pathway to the real-time prediction and avoidance of operational difficulties such as pressure outs and screen outs.
One of the challenges of unconventional resource development is the identifying and preventing casing failures caused by the hydraulic fracturing process. Multiple mechanisms may be responsible for casing deformation and/or failures, starting with the rock properties of the formation, the wellbore configuration, quality control of tubulars, and operational aspects during drilling and completion. This paper presents two case studies where casing issues were discovered during the drill out of frac plugs following multi-stage fracturing treatments. The objectives of these studies are (a) to determine the cause and nature of the casing failures, (b) to recommend changes to future completion programs to prevent similar operational issues, and (c) to develop a model that automatically identifies these failures.
The subject wells are located in two very different basins: the Eagle Ford trend in the Brazos Valley (BV) area of south Texas and the Powder River Basin (PRB) in Wyoming. In both studies, the casing issues could be directly correlated to Abnormal Pressure Behaviors (APBs) observed during fracturing. A total of 486 stages, completed in 12 different wells, were reviewed using a cloud-based application that allows stages to be examined individually, or as groups. Since then, five additional wells have been added to the data set. After problem stages were identified, the completion team worked with the drilling engineers and geologists to determine the mechanisms causing the casing damage.
Tight spots encountered during frac plug drill out in the BV wells directly correlated with stages completed in geological transition zones between the Eagle Ford and Woodbine formations. Once this was recognized, the team implemented operational contingencies to fracture designs for stages completed in BV transition zones. In the PRB wells, after reevaluating the post-mill inspection of the casing, the damage was found to be poor casing quality control. The location of casing deformations and/or failures directly correlated with stages that displayed evidence of frac plug failure. Moving forward, the PRB completion supervisors were made aware of potential issues, and alternative procedures were developed for both fracturing and drill out operations that utilized the questionable casing. As of this time, no additional casing issues have occurred.
In these studies, identification of the problem stages was initially performed manually (stage-by-stage) using a cloud-based analytics platform (CBAP). During the process, it was recognized that the two types of problem stages had their own characteristic pressure signature. A machine learning algorithm was developed that automatically identifies plug failure, which is indicated by a sudden unexplained pressure drop in the absence of rate changes. Transition stages could be easily identified through the use of stage variance plots (e.g., comparing maximum/average rates and pressures across multiple stages and wells) and also through machine learning algorithms that identified unexpected pressure increases followed by sharp pressure drops.
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).