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Dontsov, Egor (ResFrac Corporation) | Suarez-Rivera, Roberto (W. D. Von Gonten Laboratories) | Panse, Rohit (W. D. Von Gonten Laboratories) | Quinn, Christopher (W. D. Von Gonten Laboratories) | LaReau, Heather (BP America Production Company, BPx Energy Inc.) | Suter, Kirke (BP America Production Company, BPx Energy Inc.) | Hines, Chris (BP America Production Company, BPx Energy Inc.) | Montgomery, Ryan (BP America Production Company, BPx Energy Inc.) | Koontz, Kyle (BP America Production Company, BPx Energy Inc.)
Abstract As the number of wells drilled in regions with existing producing wells increases, understanding the detrimental impact of these by the depleted zone around parent wells becomes more urgent and important. This understanding should include being able to predict the extent and heterogeneity of the depleted region near the pre-existing wells, the resulting altered stress field, and the effect of this on newly created fractures from adjacent child wells. In this paper we present a workflow that addresses the above concern in the Eagle Ford shale play, using numerical simulations of fracturing and reservoir flow, to define the effect of the depletion zone on child wells and match their field production data. We utilize an ultra-fast hydraulic fracture and depletion model to conduct several hundred numerical simulations, with varying values of permeability and surface area, seeking for cases that match the field production data. Multiple solutions exist that match the field data equally well, and we used additional field production data of parent-child well-interaction, to select the most plausible model. Results show that the depletion zone is strongly non-uniform and that large reservoir regions remain undepleted. We observe two important effects of the depleted zone on fractures from child wells drilled adjacent to the parents. Some fractures propagate towards low pressure zones and do not contribute to production. Others are repelled by the higher stress region that develops around the depletion zone, propagate into undepleted rock, and have production rates commensurate to that from other child wells drilled away from depleted region. The observations are validated by the field data. Results are being used to optimize well placement and well spacing for subsequent field operations, with the objective to increase the effectiveness of the child wells.
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.
Abstract The analyses of parent-child well performance is a complex problem depending on the interplay between timing, completion design, formation properties, direct frac-hits and well spacing. Assessing the impact of well spacing on parent or child well performance is therefore challenging. A naïve approach that is purely observational does not control for completion design or formation properties and can compromise well spacing decisions and economics and perhaps, lead to non-intuitive results. By using concepts from causal inference in randomized clinical trials, we quantify the impact of well spacing decisions on parent and child well performance. The fundamental concept behind causal inference is that causality facilitates prediction; but being able to predict does not imply causality because of association between the variables. In this study, we work with a large dataset of over 3000 wells in a large oil-bearing province in Texas. The dataset includes several covariates such as completion design (proppant/fluid volumes, frac-stages, lateral length, cluster spacing, clusters/stage and others) and formation properties (mechanical and petrophysical properties) as well as downhole location. We evaluate the impact of well spacing on 6-month and 1-year cumulative oil in four groups associated with different ranges of parent-child spacing. By assessing the statistical balance between the covariates for both parent and child well groups (controlling for completion and formation properties), we estimate the causal impact of well spacing on parent and child well performance. We compare our analyses with the routine naïve approach that gives non-intuitive results. In each of the four groups associated with different ranges of parent-child well spacing, the causal workflow quantifies the production loss associated with the parent and child well. This degradation in performance is seen to decrease with increasing well spacing and we provide an optimal well spacing value for this specific multi-bench unconventional play that has been validated in the field. The naïve analyses based on simply assessing association or correlation, on the contrary, shows increasing child well degradation for increasing well spacing, which is simply not supported by the data. The routinely applied correlative analyses between the outcome (cumulative oil) and predictors (well spacing) fails simply because it does not control for variations in completion design over the years, nor does it account for variations in the formation properties. To our knowledge, there is no other paper in petroleum engineering literature that speaks of causal inference. This is a fundamental precept in medicine to assess drug efficacy by controlling for age, sex, habits and other covariates. The same workflow can easily be generalized to assess well spacing decisions and parent-child well performance across multi-generational completion designs and spatially variant formation properties.
Summary Shale, which has pores as small as 10 nm, is economically viable for hydrocarbon recovery when it is fractured. Although the fracture toughness dictates the required energy for the improvement, the existing techniques are not suitable for characterization at scales smaller than 1 cm. Developing practical methods for characterization is crucial because fractures can contribute to an accessible pore volume at different scales. This study proposes a conceptual model to characterize the anisotropic fracture toughness of shale using nanoindentations on a sub-1-cm scale. The conceptual model reveals the complexities of characterizing shales and explains why induced fractures differ from those observed in more-homogeneous media, such as fused silica. Samples from the Wolfcamp Formation were tested using Berkovich and cube-corner tips, and the interpreted fracture toughness values are promising. The conceptual model is the first application of the effective-medium theory for fracture toughness characterization using nanoindentation. In addition, it can quantify fracture toughness variations when using small samples, such as drill cuttings. Introduction Shale is a sedimentary rock containing clay minerals and silt-sized particles (Blatt and Tracy 1996) with a pore size of smaller than 100 nm in its matrix, which results in ultralow permeability. Shale gas was first extracted in 1821 (Hill et al. 2004) and has recently become economically viable because of hydraulic fracturing. This has made the US a significant fossil fuel producer. Since the Stanolind Oil and Gas Corporation performed the first hydraulic fracturing using water-based muds in 1947 (King 2012), many stimulations have shown favorable results and an increased recovery rate.
Summary The pressure decline data after the end of a hydraulic fracture stage are sometimes monitored for an extended period of time. However, to the best of our knowledge, these data are not analyzed and are often ignored or underappreciated because of a lack of suitable models for the closure of propped fractures. In this study, we present a new approach to model and analyze pressure decline data that are available in unconventional horizontal wells with multistage, transverse hydraulic fracturing. The methods presented in this study allow us to quantify closure stress and average pore pressure inside the stimulated reservoir volume (SRV) and to infer the uniformity of proppant distribution without additional data acquisition costs. For the first time, field data of diagnostic fracture injection test (DFIT), flowback, and pressure decline of main fracturing stages from the same well are compared and analyzed. We found that the early-time main fracturing stage pressure decline trend is controlled by fracture tip extension, followed by progressive hydraulic fracture closure on the proppant pack, whereas late-time pressure decline reflects linear flow. When DFIT data are not available, pressure decline analysis of a main hydraulic fracturing stage can be a substitution if it can be monitored for an extended period to allow fracture closure on proppants and asperities.
Alzahabi, A. (University of Texas-Permian Basin) | Alexandre Trindade, A. (Texas Tech University) | Kamel, A. A. (University of Texas-Permian Basin) | Harouaka, A. (University of Texas-Permian Basin) | Baustian, W. (Camino Natural Resources) | Campbell, C. (Camino Natural Resources)
Summary One of the enduring pieces of the jigsaw puzzle for all unconventional plays is drawdown (DD), a technique for attaining optimal return on investment. Assessment of the DD from producing wells in unconventional resources poses unique challenges to operators; among them the fact that many operators are reluctant to reveal the production, pressure, and completion data required. In addition to multiple factors, various completion and spacing parameters add to the complexity of the problem. This work aims to determine the optimum DD strategy. Several DD trials were implemented within the Anadarko Basin in combination with various completion strategies. Privately obtained production and completion data were analyzed and combined with well log analysis in conjunction with data analytics tools. A case study is presented that explores a new strategy for DD producing wells within the Anadarko Basin to optimize a return on investment. We use scatter-plot smoothing to develop a predictive relationship between DD and two dependent variables—estimated ultimate recovery (EUR) and initial production (IP) for 180 days of oil—and introduce a model that evaluates horizontal well production variables based on DD. Key data were estimated using reservoir and production variables. The data analytics suggested the optimal DD value of 53 psi/D for different reservoirs within the Anadarko Basin. This result may give professionals additional insight into more fully understanding the Anadarko Basin. Through these optimal ranges, we hope to gain a more complete understanding of the best way to DD wells when they are drilled simultaneously. Our discoveries and workflow within the Woodford and Mayes Formations may be applied to various plays and formations across the unconventional play spectrum. Optimal DD techniques in unconventional reservoirs could add billions of dollars in revenue to a company’s portfolio and dramatically increase the rate of return, as well as offer a new understanding of the respective producing reservoirs.
Summary The effects of temperature on the permeability coefficients of carbonaceous shales and the underlying mechanisms have been investigated experimentally. Pressure-pulse-decay gas-permeability tests were performed on seven shale plugs with different lithological compositions, organic-matter contents ranging from 0.8 to 11.7% total organic carbon (TOC) and thermal maturity between 0.53 and 1.45% random vitrinite reflectance (VRr). During the tests, the measuring temperatures were changed stepwise from 30 to 120°C and back to 30°C while axial load and confining pressure were kept constant. Sister plugs were used for mechanical tests to investigate the creep response upon thermal loading under the same temperature conditions. The samples showed varying degrees of permeability reduction by up to 71% with increasing temperature. This reduced permeability persisted during the cooling phase. The observed permeability changes reflect the elastoplastic deformation upon the thermal compaction of the rocks. Permeability reduction and creep response with increasing temperature are evidently controlled by organic matter, although clay minerals also played an important role. Organic-matter- and clay-rich shales exhibit the strongest response to temperature, while temperature effects were slightly smaller for overmature samples. Rock mechanical analysis showed that permeability reduction correlates with temperature-related creep/deformation of the shales. Given the strong temperature dependence of the mechanical stiffness of solid organic matter and of the viscosity of bituminous solids/liquids, more attention should be paid to temperature effects in the assessment of shale permeability. Our experimental results document that thermal stimulation has negative effects on shale-transport properties and that measurements conducted at laboratory temperatures can lead to substantial overestimation of in-situ shale permeability.
Summary There is a great deal of interest in the oil and gas industry (OGI) in seeking ways to implement machine learning (ML) to provide valuable insights for increased profitability. With buzzwords such as data analytics, ML, artificial intelligence (AI), and so forth, the curiosity of typical drilling practitioners and researchers is piqued. While a few review papers summarize the application of ML in the OGI, such as Noshi and Schubert (2018), they only provide simple summaries of ML applications without detailed and practical steps that benefit OGI practitioners interested in incorporating ML into their workflow. This paper addresses this gap by systematically reviewing a variety of recent publications to identify the problems posed by oil and gas practitioners and researchers in drilling operations. Analyses are also performed to determine which algorithms are most widely used and in which area of oilwell-drilling operations these algorithms are being used. Deep dives are performed into representative case studies that use ML techniques to address the challenges of oilwell drilling. This study summarizes what ML techniques are used to resolve the challenges faced, and what input parameters are needed for these ML algorithms. The optimal size of the data set necessary is included, and in some cases where to obtain the data set for efficient implementation is also included. Thus, we break down the ML workflow into the three phases commonly used in the input/process/output model. Simplifying the ML applications into this model is expected to help define the appropriate tools to be used for different problems. In this work, data on the required input, appropriate ML method, and the desired output are extracted from representative case studies in the literature of the last decade. The results show that artificial neural networks (ANNs), support vector machines (SVMs), and regression are the most used ML algorithms in drilling, accounting for 18, 17, and 13%, respectively, of all the cases analyzed in this paper. Of the representative case studies, 60% implemented these and other ML techniques to predict the rate of penetration (ROP), differential pipe sticking (DPS), drillstring vibration, or other drilling events. Prediction of rheological properties of drilling fluids and estimation of the formation properties was performed in 22% of the publications reviewed. Some other aspects of drilling in which ML was applied were well planning (5%), pressure management (3%), and well placement (3%). From the results, the top ML algorithms used in the drilling industry are versatile algorithms that are easily applicable in almost any situation. The presentation of the ML workflow in different aspects of drilling is expected to help both drilling practitioners and researchers. Several step-by-step guidelines available in the publications reviewed here will guide the implementation of these algorithms in the resolution of drilling challenges.
Abstract Geomechanical rock properties correlations and modeling approach for conventional reservoirs are inappropriate and unsuitable for unconventional shale gas reservoirs where the shale formation is strong and has very low porosity. These correlations are critical in the development of 1D and 3D geomechanical models which are used for various field applications including drilling optimization, hydraulic fracturing design and operation, and field management. The study investigates various geomechanical rock properties and their relationships to one another using data extracted from rock mechanics testing conducted on shale core samples. For rock elastic properties correlations, dynamic elastic properties determined from compressional sonic velocity, shear sonic velocity and density are plotted against laboratory-measured static elastic properties obtained from triaxial tests. Steps were taken to further refine the properties correlations by separating the data from vertical and horizontal core samples, using data from tests conducted at in-situ confining stress condition, and focusing on data only taken from Field A and nearby fields. Similar steps were also taken to develop the correlations for rock strength properties. Correlations for the shale anisotropic elastic properties were also developed based on ratio of horizontal and vertical elastic properties. Blind tests were conducted on three wells in Field A using the new rock properties correlations which showed good matching of the predicted geomechanical properties with the new correlations and core measured test data.