|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
When a panel of fracturing technology leaders was asked if classic physics-based engineering matters in engineering fracturing, the answer was a qualified "sometimes." The group of three engineers speaking at the start of the SPE Hydraulic Fracturing Technology Conference was not going to dismiss the need for physics-based modeling. Still, applying the physics of flow in a complex, fractured reservoir sounded like a wrong turn. To explain further, Cameron Rempel, vice president for subsurface engineering for Occidental Petroleum, compared analysis at the fracture level to trying to understand rush hour traffic by tracking each person as they pack up in their cubicle and head for their car at the end of the day. That example, which will someday again represent office reality, is both incredibly hard to measure and analyze and does not offer a direct path to answer an analogous question that matters to oil producers: How can we measure the time it takes for all those cars to flow out of downtown and find ways to speed them up?
Swiss oil trader Vitol said on 30 April that its oil and gas subsidiary, Vencer Energy, was buying Hunt Oil Company's assets in the Permian Basin for an undisclosed sum. Media outlets including Bloomberg and Reuters cited sources that pegged the asking price at around $1 billion. Houston-based Vencer was established last year as the trading giant's first foray into the upstream sector. The assets include leases on 44,000 acres in the Midland Basin side of the Permian, with an output about 40,000 BOE/D. "This is an important day for Vencer as it establishes itself as a significant shale producer in the US Lower 48. We expect US oil to be an important part of global energy balances for years to come, and we believe this is an opportune time for investment into an entry platform in the Americas," said Ben Marshall, the head of Vitol's Americas business unit.
Abstract Distributed Fiber Optics (DFO) technology has been the new face for unconventional well diagnostics. This technology focuses on measuring Distributed Acoustic Sensing (DAS) and Distrusted Temperature Sensing (DTS) to give an in-depth understanding of well productivity pre and post stimulation. Many different completion design strategies, both on surface and downhole, are used to obtain the best fracture network outcome; however, with complex geological features, different fracture designs, and fracture driven interactions (FDIs) effecting nearby wells, it is difficult to grasp a full understanding on completion design performance for each well. Validating completion designs and improving on the learnings found in each data set should be the foundation in developing each field. Capturing a data set with strong evidence of what works and what doesn't, can help the operator make better engineering decisions to make more efficient wells as well as help gauge the spacing between each well. The focus of this paper will be on a few case studies in the Bakken which vividly show how infill wells greatly interfered with production output. A DFO deployed with a 0.6" OD, 23,000-foot-long carbon fiber rod to acquire DAS and DTS for post frac flow, completion, and interference evaluation. This paper will dive into the DFO measurements taken post frac to further explain what effects are seen on completion designs caused by interferences with infill wells; the learnings taken from the DFO post frac were applied to further escalate the understanding and awareness of how infill wells will preform on future pad sites. A showcase of three separate data sets from the Bakken will identify how effective DFO technology can be in evaluating and making informed decisions on future frac completions. In this paper we will also show and discuss how DFO can measure real time FDI events and what measures can be taken to lessen the impact on negative interference caused by infill wells.
Guo, Yifei (The University of Texas at Austin) | Ashok, Pradeepkumar (The University of Texas at Austin) | van Oort, Eric (The University of Texas at Austin) | Patterson, Ross (Hess Corporation) | Zheng, Dandan (Hess Corporation) | Isbell, Matthew (Hess Corporation) | Riopelle, Austin (Marathon Oil Corporation)
Abstract Well interference, which is commonly referred to as frac hits, has become a significant factor affecting production in fractured horizontal shale wells with the increase in infill drilling in recent years. Today, there is still no clear understanding on how frac hits affect production. This paper aims to develop a process to automatically identify the different types of frac hits and to determine the effect of stage-to-well distance and frac hit intensity on long-term parent well production. First, child well completions data and parent well pressure data are processed by a frac hit detection algorithm to automatically identify different frac hit intensities and duration within each stage. This algorithm classifies frac hits based on the magnitude of the differential pressure spikes. The frac stage to parent well distance is also calculated. Then, we compare the daily production trend before and after the frac hits to determine the severity of its influence on production. Finally, any evident correlations between the stage-to-well distance, frac hit intensity and production change are identified and investigated. This work utilizes 3 datasets covering 22 horizontal wells in the Bakken Formation and 37 horizontal wells in the Eagle Ford Shale Formation. These sets included well trajectories, child well completions data, parent well pressure data and parent well production data. The frac hit detection algorithm developed can accurately detect frac hits in the available dataset with minimal false alerts. The data analysis results show that frac hit severity (production response) and intensity (pressure response) are not only affected by the distance between parent and child wells, but also affected by the directionality of the wells. Parent wells tend to experience more frac hits from the child frac stages with smaller direction angles and shorter stage-to-parent distances. Formation stress change with time is another factor that affects frac hit intensity. Depleted wells are more susceptible to frac hits even if they are further from the child wells. Also, we observe frac hits in parent wells due to a stimulation of a child well in a different shale formation. This paper presents a novel automated frac hit detection algorithm to quickly identify different types of frac hits. This paper also presents a novel way of carrying out production analysis to determine whether frac hits in a well have positive or negative influence long-term production. Additionally, the paper introduces the concept of the stage-to-well distance as a more accurate metric for analyzing the influence of frac hits on production.
Ajisafe, Foluke (Schlumberger) | Reid, Mark (Lime Rock Resources) | Porter, Hank (Lime Rock Resources) | George, Lydia (Former employee of Schlumberger) | Wu, Rhonna (Former employee of Schlumberger) | Yudina, Kira (Former employee of Schlumberger) | Pena, Alejandro (Schlumberger) | Ejofodomi, Efe (Schlumberger) | Artola, Pedro (Schlumberger)
Abstract Increased drilling of infill wells in the Bakken has led to growing concern over the effects of frac or fracture hits between parent and infill wells. Fracture hits can cause decreased production in a parent well, as well as other negative effects such as wellbore sanding, casing damage, and reduced production performance from the infill well. An operator had an objective to maximize production of infill wells and decrease the frequency and severity of frac hits to parent wells. The goal was to maintain production of the parent wells and avoid sanding, which had the potential to cause cleanouts. Infill well completion technologies were successfully implemented on multiwell pads in Mountrail County, Williston basin, to minimize parent-child well interference or negative frac hits on parent wells for optimized production. Four infill (child) wells were landed in the Three Forks formation directly below a group of six parent wells landed in the Middle Bakken. The infill well completion technologies used in this project to mitigate frac hits included far-field diverter, near-wellbore diverter, and real-time pressure monitoring. The far-field diverter design includes a blend of multimodal particles to bridge the fracture tip, preventing excessive fracture length and height growth. The near-wellbore diverter consists of a proprietary blend of degradable particles with a tetra modal size distribution and fibers used to achieve sequential stimulation of perforated clusters to maximize wellbore coverage. Hydraulic fracture modeling with a unique advanced particle transport model was used to predict the impact of the far-field diverter design on fracture geometry. Real-time pressure monitoring allowed acquisition of parent well pressure data to identify pressure communication or lack of communication and implement mitigation and contingency procedures as necessary. Real-time pressure monitoring was also used to optimize and validate the far-field diversion design during the job execution. The parent well monitored was 800 ft away from the closest infill well and at high risk for frac hits due to both the proximity to the infill well and depletion. In the early stages of the infill well stimulation, an increase in pressure up to 600 psi was observed in the parent well. The far-field diverter design was modified to combat the observed frac hit, after which a noticeable drop in both frequency and magnitude of frac hits was observed on the parent well. This is the first time the far-field diverter design optimization process was done in real time. After the infill wells stimulation treatment, production results showed a positive uplift in oil production for all parent wells at an average of 118%. Also, only two out of seven parent wells required a full cleanout, resulting in savings in well cleanup costs. Infill well production data was compared with the closest parent well landed in the same formation (Three Forks). At about a year, the best infill well production was only 10% less than the parent well with similar completion design and the average infill well production approximately 18% less than the parent well. Considering the depletion surrounding the infill wells, production performance exceeded expectations.
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.
Abstract Reducing well costs in unconventional development while maintaining or improving production continues to be important to the success of operators. Generally, the primary drivers for oil and gas production are treatment fluid volume, proppant mass, and the number of stages or intervals along the well. Increasing these variables typically results in increased costs, causing additional time and complexity to complete these larger designs. Simultaneously completing two wells using the same volumes, rates, and number of stages as for any previous single well, allows for more lateral length or volume completed per day. This paper presents the necessary developments and outcomes of a completion technique utilizing a single hydraulic fracturing spread to simultaneously stimulate two or more horizontal wells. The goal of this technique is to increase operational efficiency, lower completion cost, and reduce the time from permitting a well to production of that well—without negatively impacting the primary drivers of well performance. To date this technique has been successfully performed in both the Bakken and Permian basins in more than 200 wells, proving its success can translate to other unconventional fields and operations. Ultimately, over 200 wells were successfully completed simultaneously, resulting in a 45% increase in completion speed and significant decrease in completion costs, while still maintaining equivalent well performance. This type of simultaneous completion scenario continues to be implemented and improved upon to improve asset returns.
Rodríguez-Pradilla, Germán (School of Earth Sciences, University of Bristol, UK.) | Eaton, David (Department of Geoscience, University of Calgary, Canada.) | Popp, Melanie (geoLOGIC Systems Ltd., Calgary, Canada.)
Abstract 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 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.
Shale producer Oasis Petroleum said Monday that it is acquiring Williston Basin assets from Diamondback Energy in a cash deal valued at $745 million. Oasis will take on about 17,700 B/D in existing oil production on 95,000 net acres of leases at a cost of nearly $28,000 per BOED, the company said in its announcement. The acquisition's production will add to the operator's first-quarter base of about 36,800 B/D of oil, bringing pro forma production to an estimated 54,500 B/D. "This exciting acquisition is a great example of how Oasis is addressing the needs of tomorrow, by taking action in our new industry paradigm, today," Danny Brown, Oasis' CEO, said in a statement. "This acquisition materially enhances scale in our core Bakken asset at an attractive valuation, with the purchase price almost entirely based on PDP and very little value attributed to the development of the top-tier inventory or potential synergies," he continued.
Summary In this work, we investigate the efficient estimation of the optimal design variables that maximize net present value (NPV) for the life-cycle production optimization during a single-well carbon dioxide (CO2) huff-n-puff (HnP) process in unconventional oil reservoirs. A synthetic unconventional reservoir model based on Bakken Formation oil composition is used. The model accounts for the natural fracture and geomechanical effects. Both the deterministic (based on a single reservoir model) and robust (based on an ensemble of reservoir models) production optimization strategies are considered. The injection rate of CO2, the production bottomhole pressure (BHP), the duration of injection and the production periods in each cycle of the HnP process, and the cycle lengths for a predetermined life-cycle time can be included in the set of optimum design (or well control) variables. During optimization, the NPV is calculated by a machine learning (ML) proxy model trained to accurately approximate the NPV that would be calculated from a reservoir simulator run. Similar to the ML algorithms, we use both least-squares (LS) support vector regression (SVR) and Gaussian process regression (GPR). Given a set of forward simulation runs with a commercial compositional simulator that simulates the miscible CO2 HnP process, a proxy is built based on the ML method chosen. Having the proxy model, we use it in an iterative-sampling-refinement optimization algorithm directly to optimize the design variables. As an optimization tool, the sequential quadratic programming (SQP) method is used inside this iterative-sampling-refinement optimization algorithm. Computational efficiencies of the ML proxy-based optimization methods are compared with those of the conventional stochastic simplex approximate gradient (StoSAG)-based methods. Our results show that the LS-SVR- and GPR-based proxy models are accurate and useful in approximating NPV in the optimization of the CO2 HnP process. The results also indicate that both the GPR and LS-SVR methods exhibit very similar convergence rates, but GPR requires 10 times more computational time than LS-SVR. However, GPR provides flexibility over LS-SVR to access uncertainty in our NPV predictions because it considers the covariance information of the GPR model. Both ML-based methods prove to be quite efficient in production optimization, saving significant computational times (at least 4 times more efficient) over a stochastic gradient computed from a high-fidelity compositional simulator directly in a gradient ascent algorithm. To our knowledge, this is the first study presenting a comprehensive review and comparison of two different ML-proxy-based optimization methods with traditional StoSAG-based optimization methods for the production optimization problem of a miscible CO2HnP.