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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.
Suarez-Rivera, Roberto (W. D. Von Gonten Laboratories) | Panse, Rohit (W. D. Von Gonten Laboratories) | Sovizi, Javad (Baker Hughes) | Dontsov, Egor (ResFrac Corporation) | LaReau, Heather (BP America Production Company, BPx Energy Inc.) | Suter, Kirke (BP America Production Company, BPx Energy Inc.) | Blose, Matthew (BP America Production Company, BPx Energy Inc.) | Hailu, Thomas (BP America Production Company, BPx Energy Inc.) | Koontz, Kyle (BP America Production Company, BPx Energy Inc.)
Abstract 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.
ABSTRACT 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
Abstract Sonic data are commonly acquired in exploration, appraisal, and development wells using wireline, logging-while-drilling, or through-the-bit conveyance for applications within petrophysics, geophysics, geomechanics, and geology disciplines. The measurement data require processing to obtain elastic wave slownesses (inverse of velocity) and associated attributes before the results can be used in petrotechnical workflows. The objective of the digital transformation is to streamline and automate the processing workflow to reduce user intervention and turnaround time while increasing the accuracy of results and possibly extracting more answers by fully utilizing all waveform attributes, which consequently benefits downstream applications. There are four workflows that are the foundation of the transformation. They support the overall goals of reducing user interactions and providing robust results in a timely manner for continuous slowness logs. First, data-driven inversions done during acquisition with automatic quality control and interpretation flags immediately provide assurance about the data quality and identify formation intervals that require further evaluation. Second, automatic dipole-flexural shear extraction is done using physics-based machine learning (ML) where purely data-driven models are inadequate due to borehole or geological conditions. The physics-based ML utilizes cloud-based computing that is needed for large volume synthetic data generation and neural network training. Third, a multiresolution analysis of the monopole waves for the compressional slowness uses automatic peak detection on multiple receiver levels removing any subjective manual labeling after the semblance processing. Finally, the multimode (flexural and Stoneley) inversion determines anisotropic constants and accounts for mud-speed variations in the borehole, including detailed uncertainties. The new methods address underlying concerns most users and waveform processing experts already observe in their sonic deliverables. Enabling wellsite algorithms to be more automatic and data driven improves the robustness of the field deliverables and provides insight into the quality of the data. For the shortcomings with regards to borehole or geological conditions such as laminations, sharp lithological transitions, or the presence of anisotropy, the physics-based ML is shown to honor the physics of the dipole flexural mode, while the multiresolution for the monopole provides physics-based reasoning for discrepancies between the geological layering and receiver aperture. By incorporating the range of results derived from the inversions with advanced interpretations such as transversely isotropic constants, these uncertainties can be further used in stochastic models in downstream workflows. All these methods are fully automated and can be done in a short timeframe to be used without doubt in operations.
Abstract In recent years, well performance from tight reservoirs in the Delaware Basin has been improving due to enhanced completion practices, better reservoir targeting and improved well designs in the region. One of the key components to the enhanced completion practices has been the implementation of progressively longer laterals. The rate of increase in lateral lengths have slightly slowed in recent years, as operators approach the point of no additional value creation as the well costs supersede the production gained from longer wells. This paper presents a tool created to evaluate the performance and economics of a given well given different reservoir, fluid, well design and completion parameters. The tool is also a probabilistic model that can quantify the impact of input parameters that the user feels uncertain about. As a result, it can provide management teams with an approach to make capital decisions under uncertainty. The proposed methodology presented in this paper is repeatable for different tight rock formations across different basins. An example of the tool's capability is demonstrated in this paper using an asset profile typical of the Delaware Basin's Wolfcamp A.
Pei, Yanli (The University of Texas at Austin) | Yu, Wei (The University of Texas at Austin / Sim Tech LLC) | Sepehrnoori, Kamy (The University of Texas at Austin) | Gong, Yiwen (Sim Tech LLC / The Ohio State University) | Xie, Hongbin (Sim Tech LLC) | Wu, Kan (Texas A&M University)
The extensive depletion of the development target has triggered the demand for infill drilling in the upside target of multilayer unconventional reservoirs. To optimize the hydraulic fracturing design of newly drilled wells, we need to investigate the stress changes in the upside target induced by parent-well production. In this work, an integrated parent-child workflow is presented to model the spatial-temporal stress evolution and propose the optimal development strategy for the upside target using a data set from the Permian Basin. The stress dependence of matrix permeability and fracture conductivity is determined based on available experimental data and incorporated in our reservoir simulation with the aid of an embedded discrete fracture model (EDFM). With calibrated reservoir properties from history matching of an actual well in the development target (i.e., 3rd BS Sand), we run the finite element method (FEM) based geomechanics simulator to predict the 3D spatial-temporal evolution of the local principal stresses. A displacement discontinuity method (DDM) hydraulic fracture model is then applied to simulate the multi-cluster fracture propagation in the upside target (i.e., L2BSSh) with the updated heterogeneous stress field. Numerical results indicate that stress field redistribution associated with parent-well production not only occurs within the development target but also vertically propagates to the upside target. A smaller parent-child horizontal offset induces a severer deviation of child-fractures towards the parent wellbore, resulting in more substantial well interference and less desirable oil and gas production. The parent-child fracture overlapping ratio in our study is in 0.6 ~ 0.8 for the 400 ft horizontal offset and 0.2 ~ 0.5 for the 600 ft horizontal offset. Varying the parent-child vertical offset gives the same trend as we change the horizontal offset. But with a delayed infill time, placing child-well in different layers causes more significant variation in the ultimate recovery. Moreover, infill operations at an earlier time are less affected by parent-well depletion because of the more homogeneous stress state. The candidate locations to implement infill-wells are suggested in the end for different infill timing by co-simulation of the parent-child production. With the reservoir-geomechanics-fracture model, this work provides a general workflow to optimize the child-well completion in multilayer unconventional reservoirs. The conclusions drawn from this study are of guiding significance to the subsequent development in the Permian Basin.
Given the state of the oil & gas industry today, i.e., low hydrocarbon prices and a global health crisis still in high gear, making good business decisions is more crucial than ever. Deciding which wells to keep open for production, which wells to shut-in, which wells to re-stimulate for immediate production, and which new wells to drill, if any, may directly impact a business' financial survival. This is true for both conventional and unconventional assets, but of significantly more concern to the unconventional asset investor, because incremental production is already capital-intensive at the best of times. Over the last decade, unconventional resources have become a significant source of the total production output in various parts of the world, and the primary stimulation treatment used is hydraulic fracturing. This technique sections a wellbore into multiple stages into which highly pressurized fluid is pumped at various fracture initiation locations. Historically, the number of stages and the number of clusters per stage, has primarily been based on total lateral length, previous experience in the same or similar fields, and on investment considerations, with a strong tendency towards decreasing stage and fracture spacing (i.e., increasing stage and fracture count). Field experience showing non-productive and full-physics simulations suggest room for improvement and indicate that there must be an optimal stimulation treatment that maximizes profit. Beyond this point, adding another stage in the treatment becomes more expensive than what can be recuperated by incrementally increased production. Thus, in the current work, the problem is posed as a classic constrained optimization problem and solved using Monte Carlo methods. Results show that in general, profitability of the production revenue is very sensitive to the reservoir recovery factor, porosity, drainage volume for the lease window, and, ultimately, the market price. Introduction Unconventional wells are challenging in many ways, and significant capital investment combined with relatively short production periods makes exploitation of these types of reservoirs a balancing act between costs and profit. Wells can run in the millions when drilling and completion costs are accounted for, with completion costs accounting for more than half of the capital requirement (EIA 2016). Fortunately, the completion details are one of the few inputs that can be adjusted in the field, which allows for fine-tuning to local conditions. In this work, we employ hydraulic fracturing as the stimulation technique, and note that it is the most common type of completion technique currently in use. During hydraulic fracturing, fluid is injected into a wellbore at high pressure to create cracks in the sub-surface in the neighborhood of the wellbore, through which natural gas and oil flow more freely than through the low-permeability formations typical of unconventional reservoirs. The pressurized fluid typically carries propping material such as sand, which is intended to hold open fractures after fracturing pressure is relieved and shut-in begins. The origins of hydraulic fracturing date back to early experiments in the 1940s at the Hugoton gas field in Grant County of southwestern Kansas by Stanolind (Charlez 1997; Montgomery et al. 2010), and one of the first commercially successful applications of the new technology in the 1950s. As of 2012, about 2.5 million "frac jobs" had been performed worldwide on oil and gas wells; over one million of those within the U.S. (King 2012). In years past, such stimulation treatment was generally necessary to achieve profitable flow rates in shale gas, tight gas, tight oil, and coal seam gas wells (Charlez 1997), but in today's market environment, using the optimal stimulation treatment is all but economic requirement for economic survival.
ABSTRACT It is standard practice to estimate porosity, water saturation, and mineralogy of formations with complex solid and fluid compositions by minimizing the error between well logs and their numerical simulations, subject to realistic petrophysical and material-balance constraints. For unconventional organic-shale formations, the minimization is usually performed using Bayesian inversion, which incorporates field-specific a priori correlations and uncertainty quantification. However, no efficient multi-well application of this method is yet available because of the high computational cost associated with Markov chain Monte Carlo (McMC) sampling. Additionally, in many cases, the available well logs/core data are not sufficient to obtain accurate petrophysical estimations. We introduce and successfully verify a new workflow for rapid multi-well interpretation of wireline logs and core data. First, well logs are corrected for tool and borehole effects (e.g. shoulder beds, laterolog vs. induction resistivity tools, LWD measurements, etc.) by running separate inversions. This step outputs layer-by-layer physical properties (e.g. resistivity, gamma ray, density, and neutron migration length) that are subsequently combined to estimate compositional/petrophysical properties via Bayesian joint inversion. As a necessary step in the preparation for joint inversion, we establish petrophysical models and statistical relationships among different components in key wells where core data and spectroscopy measurements are available. Finally, we apply our McMC Bayesian joint inversion to the same formation penetrated by other nearby wells with limited measurements. Our interpretation workflow circumvents multiple difficulties for multi-well interpretation. First, performing separate inversions ensures that the input layer-by-layer physical properties are free of any uncertainty caused by shoulder beds and different borehole instruments or drilling conditions. Second, to speed up calculations, we implement the quasi-Newton McMC sampling technique and use a pre-computed surrogate model for nuclear properties. This combination reduces the computational time by a factor of 30 and 100, respectively. Third, we adopt an extended Bayesian framework that automatically implements different petrophysical models for each rock type. The proposed method is validated with a synthetic example and wireline measurements acquired across two wells in the Wolfcamp shale formation. Results show that 80% of the core data are within the 80% confidence intervals of the estimations.
Summary The low productivity of the oil wells in the Tuscaloosa Marine Shale (TMS) Trend, located in Louisiana and Mississippi, is a mystery. Production data from 55 wells in the TMS Trend were analyzed to identify possible means of enhancing well productivity. The −1/2 slope in a log–log plot for reservoir linear flow (RLF) was observed for some wells, but not all, and the −1 slope for boundary–dominated flow (BDF) has not been seen in the past 5 years of production. The behavior of the TMS wells is attributed to the ultralow permeability of the TMS matrix and the decline of fracture conductivity, both of which delay the BDF. On the basis of the concept of the distance of investigation, derived from the radius–of–investigation concept, the matrix permeability of the seven TMS wells was estimated to be between 34 and 65 nd, with an average of 51 nd. A new mathematical model for production decline of multifractured horizontal wells was developed taking into consideration the time–dependent fracture conductivity during the BDF period. This model fits TMS–well production data, with an average error of 6.12% and an R value of 0.96. Assuming constant matrix permeability, fitting the new model to the production data in the BDF period gives a rate of fracture–conductivity decline of between 0.20 and 0.74% per month. The new mathematical model reveals opportunities to optimize well completion to enhance well productivity in the TMS Trend. For a given amount of fracture proppant allocated to a well, the shortest possible fracture spacing should be used to maximize well productivity. If the number of fractures is fixed as a constraint of well completion design, well productivity is inversely proportional to the square root of fracture spacing (i.e., if the fracture spacing is shortened by fourfold, well productivity is expected to double). If the horizontal wellbore length is fixed as a constraint of well completion, well productivity is inversely proportional to fracture spacing (i.e., if the fracture spacing is shortened by 50%, well productivity is expected to double).
This paper seeks answers, through a'philosophical' approach, to the questions of whether enhanced oil recovery projects are purely driven by economic restrictions (i.e. oil prices) or if there are still technical issues to be considered, making companies refrain from enhanced oil recovery (EOR) applications. Another way of approaching these questions is to ask why some EOR projects are successful and long-lasting regardless of substantial fluctuations in oil prices. To find solid answers to these two, by'philosophical' reasoning, further questions were raised including: (1) has sufficient attention been given to the'cheapest' EOR methods such as air and microbial injection, (2) why are we afraid of the most expensive miscible processes that yield high recoveries in the long run, or (3) why is the incubation period (research to field) of EOR projects so lengthy? After a detailed analysis using sustainable EOR example cases and identifying the myths and facts about EOR, both answers to these questions and supportive data were sought. Premises were listed as outcomes to be considered in the decision making and development of EOR projects. Examples of said considerations include: (1) Every EOR process is case-specific and analogies are difficult to make, hence we still need serious efforts for project design and research for specific processes and technologies, (2) discontinuity in fundamental and case-specific research has been one of the essential reasons preventing the continuity of the projects rather than drops in oil prices, and (3) any EOR project can be made economical, if technical success is proven, through proper optimization methods and continuous project monitoring whilst considering the minimal profit that the company can tolerate. Finally, through the'philosophical' reasoning approach and using worldwide successful EOR cases, the following three parameters were found to be the most important factors in running successful EOR applications, regardless of oil prices and risky investment costs, to extend the life span of the reservoir and warrant both short and long-term profit: (1) Proper technical design and implementation of the selected EOR method through continuous monitoring and re-engineering the project (how to apply more than what to apply), (2) good reservoir characterization and geological descriptions and their effect on the mechanics of the EOR process, and (3) paying attention to experience and expertise (human factor). It is believed that the systematic analysis and philosophical approach followed in this paper and the outcome will provide proper guidance to EOR projects for upcoming decades. 2 SPE-196362-MS