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Collaborating Authors
Hoda, Sam
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.
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Hawkville Field > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Eagle Ford Shale Formation (0.99)
- (27 more...)
Abstract 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.
- North America > United States > West Virginia > Appalachian Basin (0.99)
- North America > United States > Virginia > Appalachian Basin (0.99)
- North America > United States > Tennessee > Appalachian Basin (0.99)
- (39 more...)
Abstract One component of the modern hydraulic fracturing process is streaming (continuously generating and transmitting) high-frequency data from the field to remote locations for monitoring, storage, and analysis. Analyzing this data in real-time can be especially challenging during zipper fracturing operations, which involve several wells and potentially more than one frac crew. Accurate and consistent event identification such as stage start and end times enable real-time reporting of important stage metrics, including pressures, rates, and concentrations. More advanced workflows allow real-time stage comparisons aligned on identified events such as the start of each stage, achieving target rate, and breakdown. This study aims to demonstrate an automation process to identify accurate and consistent stage start and end times in real-time using signal processing techniques. The dataset includes two types of data: post-stage treatment data including treating pressures and slurry rates for 1,151 stages from all major North American basins; and 15,000+ hours of real-time data that includes streams from zipper frac operations. In addition to the well-timing challenges, the raw field data can be very noisy, making it difficult for automated techniques to separate real events from false positives. The authors use signal processing techniques to mitigate noise, easily accommodate business rules, and follow the subject matter experts’ decision logic. The authors designed several auxiliary channels to identify approximate windows of time where the frac crew is pumping and not pumping. Many of these derived channels involve smoothing the original signals and depend on the degree of noise and whether to incorporate information about the past or the near future. Once these windows are identified, a search procedure is used to find the precise boundary between pumping times and non-pumping times (similar to zooming in on a treatment plot). The algorithm mimics an expert by identifying the relevant portions of the plot, thereby avoiding gross errors, and then zooms into the correct interval to refine its choice. Due to time constraints, and limited data viewing resolution capabilities in many frac vans, on-site frac supervisors often do not give proper attention to event tagging. As such, the algorithm’s choices are more precise (∼5 seconds of the actual event) than the average human performance (20-30 seconds). This is the first time a signal processing approach has been applied to identify key hydraulic fracturing events in a real-time data stream. This approach provides a robust, automated, transparent (white box), and extremely performant model that easily accommodates operating constraints. In turn, this enables real-time reporting of operational metrics and more advanced analyses, like comparing stages aligned on events. Providing accurate details for pump times and related operating metrics ultimately helps improve the operation’s execution and reduces completion costs.