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Horizontal wells are high-angle wells (with an inclination of generally greater than 85) drilled to enhance reservoir performance by placing a long wellbore section within the reservoir. Horizontal Well contrasts with an extended-reach well, which is a high-angle directional well drilled to intersect a target point. There was relatively little horizontal drilling activity before 1985. The Austin Chalk play is responsible for the boom in horizontal drilling activity in the U.S. Now, horizontal drilling is considered an effective reservoir-development tool. Horizontal wells are normally characterized by their buildup rates and are broadly classified into three groups that dictate the drilling and completion practices required, as shown in Table 1.
A directional well can be divided into three main sections--the surface hole, overburden section, and reservoir penetration. Different factors are involved at each stage within the overall constraints of optimum reservoir penetration. Most directional wells are drilled from multiwell installations, platforms, or drillsites. Minimizing the cost or environmental footprint requires that wells be spaced as closely as possible. It has been found that spacing on the order of 2 m (6 ft) can be achieved.
Degenhardt, John J. (W. D. Von Gonten Laboratories) | Ali, Safdar (W. D. Von Gonten Laboratories) | Ali, Mansoor (W. D. Von Gonten Laboratories) | Chin, Brian (W. D. Von Gonten Laboratories) | Von Gonten, W. D. (W. D. Von Gonten Laboratories) | Peavey, Eric (Shell Fellow-UROC / Texas A&M University)
Abstract Many unconventional reservoirs exhibit a high level of vertical heterogeneity in terms of petrophysical and geo-mechanical properties. These properties often change on the scale of centimeters across rock types or bedding, and thus cannot be accurately measured by low-resolution petrophysical logs. Nonetheless, the distribution of these properties within a flow unit can significantly impact targeting, stimulation and production. In unconventional resource plays such as the Austin Chalk and Eagle Ford shale in south Texas, ash layers are the primary source of vertical heterogeneity throughout the reservoir. The ash layers tend to vary considerably in distribution, thickness and composition, but generally have the potential to significantly impact the economic recovery of hydrocarbons by closure of hydraulic fracture conduits via viscous creep and pinch-off. The identification and characterization of ash layers can be a time-consuming process that leads to wide variations in the interpretations that are made with regard to their presence and potential impact. We seek to use machine learning (ML) techniques to facilitate rapid and more consistent identification of ash layers and other pertinent geologic lithofacies. This paper involves high-resolution laboratory measurements of geophysical properties over whole core and analysis of such data using machine-learning techniques to build novel high-resolution facies models that can be used to make statistically meaningful predictions of facies characteristics in proximally remote wells where core or other physical is not available. Multiple core wells in the Austin Chalk/Eagle Ford shale play in Dimmitt County, Texas, USA were evaluated. Drill core was scanned at high sample rates (1 mm to 1 inch) using specialized equipment to acquire continuous high resolution petrophysical logs and the general modeling workflow involved pre-processing of high frequency sample rate data and classification training using feature selection and hyperparameter estimation. Evaluation of the resulting training classifiers using Receiver Operating Characteristics (ROC) determined that the blind test ROC result for ash layers was lower than those of the better constrained carbonate and high organic mudstone/wackestone data sets. From this it can be concluded that additional consideration must be given to the set of variables that govern the petrophysical and mechanical properties of ash layers prior to developing it as a classifier. Variability among ash layers is controlled by geologic factors that essentially change their compositional makeup, and consequently, their fundamental rock properties. As such, some proportion of them are likely to be misidentified as high clay mudstone/wackestone classifiers. Further refinement of such ash layer compositional variables is expected to improve ROC results for ash layers significantly.
Abstract Assessment of effective mechanical properties such as elastic properties and brittleness can be challenging in the presence of complex rock composition, pore structure, and spatial distribution of minerals, especially in the absence of acoustic measurements. Conventional methods such as effective medium modeling, are not reliable for assessments of mechanical properties in complex formations such as carbonates, because solid skeleton of carbonates does not consist of granular minerals with ideal shapes. The effective medium models also overlook both the spatial distribution of petrophysical properties, and the coupled hydraulic and mechanical (HM) processes, which causes significant uncertainties in geomechanical evaluations. The objective of this paper is to develop a numerical method to enhance assessment of effective mechanical properties of anisotropic and heterogenous carbonate formations by modeling the variation of effective stress and the evolution of corresponding strain. The developed method takes into account the coupled HM processes, the realistic spatial distribution of rock inclusions (i.e., rock fabrics), dynamic fluid flow, pore pressure, and pore structure. To achieve this objective, we develop a pore-scale numerical simulator by satisfying conservation equations and considering the coupling among relevant HM phenomena. We adopt peridynamic theory to discretize the micro-scale medium. The inputs to our numerical modeling include pore-scale images of rock samples as well as mechanical and hydraulic properties of each rock inclusion. We perform image processing on micro-CT scan images of rock samples to obtain a realistic micro-scale structure of both rock matrix (i.e., concentration, spatial distribution, and shape of rock constituents) and pore space. We then assign realistic mechanical and hydraulic properties to each rock constituent within the pore-scale medium. The outcomes of numerical modeling include the variation of effective stress and the evolution of corresponding strain by honoring the variability in mechanical/hydraulic properties of rock inclusions caused by their spatial distribution, pore pressure, pore structure, natural fractures, and dynamic fluid flow at the micro-scale domain. We then compare the outcomes of numerical models with the mechanical properties estimated based on effective medium models.
Brinkley, Kourtney (Devon Energy) | Ingle, Trevor (Devon Energy) | Haffener, Jackson (Devon Energy) | Chapman, Philip (Devon Energy) | Baker, Scott (Devon Energy) | Hart, Eric (Devon Energy) | Haustveit, Kyle (Devon Energy) | Roberts, Jon (Devon Energy)
Abstract This case study details the use of Sealed Wellbore Pressure Monitoring (SWPM) to improve the characterization of fracture geometry and propagation during stimulation of inter-connected stacked pay in the South Texas Eagle Ford Shale. The SWPM workflow utilizes surface pressure gauges to detect hydraulically induced fracture arrivals athorizontal monitor locations adjacent to the stimulated wellbore (Haustveit et al. 2020). A stacked and staggered development in Dewitt County provided the opportunity to jointly evaluateprimary completion and recompletion efforts spanning three reservoir target intervals. Fivemonitor wells at varying distances across the unit were employed for SWPM during the stimulation of four wells. An operational overview, analysis of techniques, correlation with seismic attributes, image log interpretations, and fracture model calibration are provided. Outputs from this workflow allow for a refined analysis ofthe overall completion strategy. The high-density, five well monitor array recorded a total of 160 fracture arrivals at varying vertical and lateral distances, with far-field fracture arrivalsprovidingsignificant insight into propagation rates and geometry. Apronounced trend occurred in both arrival frequency and volumes pumped as monitor locations increased in distance from the treatment well. Specific to target zone isolation, it was identified that traversing vertically in section through a high stress interval yielded a 30% reduction inarrival frequency. An indirect relationship between horizontal distance and arrival frequency was also observed when monitoring from the same interval. A decrease in fracture arrivals from 70% down to 8% was realized as offset distance increased from 120 to 1,700 ft. The results from this study have proven to be instrumental in guiding interdisciplinary discussion. Assessing fracture geometry and propagation during stimulation, particularly in the co-development of a stacked pay reservoir, is paramount to the determination of proper completion volume, perforation design, and well spacing. Leveraging the observations of SWPM ultimately provides greater confidence in field development strategy and economic optimization.
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.
Although unconventional resources contributed almost no production in the early 1970s, they now provide almost 30% of US domestic natural gas supply. Unconventional reservoirs have a complex lithology with heterogeneous porosity, complex fluids, or both. Ultimate recovery is typically lower than from conventional reservoirs, so effective exploitation requires more wells to drain the resource effectively. Excluding product price, capital expenditures are a critical factor in the economics and have driven a development culture focused on cost, in the absence of technology that provides a clear cost benefit to increased production and/or recovery. However, the exponential growth of production from unconventional reservoirs has not been accompanied by comparable growth in the knowledge and understanding of the rock properties and completion parameters controlling production.
The most read stories on JPT's website in 2019 underscore the technological and operational trends shaping the upstream industry as it enters a new decade. Among the most popular topics: Companies that are leading the industrywide digital transformation, the movement to share more data, the energy transition and its impact on petroleum engineers, and figuring out how to improve efficiency, safety, and productivity to sustain shale operations. The UK Oil and Gas Authority earlier this year made available to the public some 130 terabytes of well, geophysical, field, and infrastructure data through the launch of the UK Oil and Gas National Data Repository, capturing the imaginations of the upstream industry both in the country and abroad. BP's Energy Outlook 2019, released in February, could be reduced to a single question: Is petroleum engineering a good, long-term career choice for a college student? Pressure pumping equipment has been one of the most neglected areas of technological advancement.
Equinor has agreed to sell its 63% interest and operatorship to partner Repsol in their joint venture (JV) targeting the Eagle Ford Shale of south Texas. The $325-million deal consists of 69,000 net acres and production of 34,000 BOE/D. Madrid-based Repsol will become 100% owner of the acreage, which produces 54,000 BOE/D in total. The deal is effective 1 October. "This transaction supports Equinor's strategy to optimize our onshore US portfolio, enhancing our financial flexibility and focusing our capital on our core activities in the country," said Torgrim Reitan, Equinor executive vice president for international development and production, in a news release.