Wolfcamp Formation Research Articles

Enhancing Well Performance and Economic Value via Better Well Completion Design—A Delaware Basin Case Study

Summary Operator H’s wells on University Lands acreage in the Delaware Basin were completed with a perforation cluster spacing of around 45 ft and a fracturing fluid volume of about 25 bbl/ft. However, other operators in this area used half or even smaller perforation cluster spacing and a few times more fluid. As a result, the estimated ultimate recovery (EUR) of non-Operator H wells is about 18% higher than that of Operator H’s wells in the Wolfcamp Formation, indicating that Operator H’s completion design can be greatly improved. Therefore, our objective in this work is to locate a better completion design for Operator H’s wells and increase the economic value of the operator and the landowner. An integrated fracturing→well performance→economics workflow is implemented to optimize the completion design for Operator H’s wells. First, a 3D geological and geomechanical model is built and calibrated with historic multiwell multistage completion and production data. A decline curve analysis (DCA) is also performed to double-check our history-matching (HM) model. Then, we use an unconventional fracturing model to simulate fracture propagation and proppant transport for multiple wells on the same pad under various completion designs. The resultant complex fracture network is converted into unstructured gridblocks for reservoir-performance simulation. Finally, economic analysis is incorporated to evaluate the net present value (NPV)and internal rate of return (IRR) to identify the optimal completion design. Numerical results show that with the same amount of fluid and proppant, halving perforation cluster spacing gives shorter and more nonuniform fractures. Doubling the fluid volume creates 40% more fracture area in our target area and activates more microfractures along the wellbore. Also, a smaller stage spacing significantly improves cluster efficiency and well production. For the Wolfcamp Formation in the Delaware Basin, tighter cluster spacing (10–20 ft) with higher fluid volume (75–90 bbl/ft) plus a smaller stage spacing (~120 ft) greatly improves fracturing efficiency and well performance. Our recommended optimal completion design would increase Operator H’s NPV10 by USD 226 million on its undeveloped Wolfcamp A Formation (WCA). With the latest modeling technologies, we model the complex fracture propagation and associated well performance and efficiently identify a better completion design with economic analysis. Compared to multiple field pilot tests needed, the integrated workflow in this work helps operators significantly reduce the time and financial cost to optimize completion design and can be easily adapted to other unconventional wells given their unique data set.

Applying Reservoir Simulation and Artificial Intelligence Algorithms to Optimize Fracture Characterization and CO2 Enhanced Oil Recovery in Unconventional Reservoirs: A Case Study in the Wolfcamp Formation

Reservoir simulation for unconventional reservoirs requires proper history matching (HM) to quantify the uncertainties of fracture properties and proper modeling methods to address complex fracture geometry. An integrated method, namely embedded discrete fracture model–artificial intelligence–automatic HM (EDFM–AI–AHM), was used to automatically generate HM solutions for a multistage hydraulic fracturing well in the Wolfcamp Formation. Thirteen scenarios with different combinations of matrix and fracture parameters as variables or fixed inputs were designed to generate 1300 reservoir simulations via EDFM–AI–AHM, from which 358 HM solutions were retained to reproduce production history and quantify the uncertainties of matrix and hydraulic fracture properties. The best HM solution was used for production forecasting and carbon dioxide (CO2)-enhanced oil recovery (EOR) strategy optimization. The results of the production forecast for primary recovery indicated that the drainage area for oil production was difficult to extend further into the low-permeability reservoir matrix. However, CO2 EOR simulations showed that increasing the gas injection rate during the injection cycle promoted incremental oil production from the reservoir matrix, regardless of minimum miscibility pressure. A gas injection rate of 25 million standard cubic feet per day (MMscfd) resulted in a 14% incremental oil production improvement compared to the baseline scenario with no EOR. This paper demonstrates the utility of coupling reservoir simulation with artificial intelligence algorithms to generate ensembles of simulation cases that provide insights into the relationships between fracture network properties and production.

Sealing fractures to increase underground storage security: Lessons learned from a multiscale multimodal imaging study of a syntaxial vein in a mudrock

Veins recovered from deep sedimentary formations offer insights into mineral precipitates and processes that lead to sealing of underground fractures. These processes are of interest in the context of subsurface negative emissions technologies such as geologic carbon storage, where fractures are potential pathways for unwanted fluid migration. In this study, we characterized the mineralogy and porosity of a syntaxial vein in a mudrock sample from the Wolfcamp formation in Texas. The original fracture had an aperture of 5 mm and is now filled with distinct zones of minerals and vuggy regions. Thin sections from cuts across the vein were examined at micron scale resolution using scanning electron microscopy, energy dispersive X-ray spectroscopy, QEMSCAN, and polarized light microscopy. Larger-scale analyses were done using synchrotron X-ray fluorescence. Collectively, these methods reveal elongated crystals of dolomite as large as 900 μm, overlain with a mixture of smaller crystals including calcite and ferroan dolomite. Silica fills some of the void space. Mineral identification was corroborated using powder X-ray diffraction. Quantitative analysis of a 3D X-ray computed tomography image indicates that the vein volume contains 62% elongate dolomite crystals, 33% mixed ferroan dolomite and calcite, 1% silica, and 4% vuggy void space. Synchrotron small and ultra-small angle X-ray scattering reveals that the vein mineral precipitates have ~1% porosity. This is much smaller than the porosity of the mudrock matrix. The findings in this study suggest that as the formation formed and subsided, fracture fluids migrated vertically and experienced pressure reduction causing exsolution of CO2. A geochemical simulation demonstrated how this could have led to carbonate precipitation in the veins. A fundamental understanding of the sequence of vein mineral precipitation and the associated reduction in porosity may inspire strategies designed to induce fracture sealing, thus preserving the integrity of underground fluid storage. Changes that would lead to CO2 exsolution, carbonate supersaturation, and mineral precipitation include increasing the pH, addition of divalent cations, enabling vertical migration with subsequent depressurization, and heating to reduce carbonate solubility.

Gaussian Decline Curve Analysis of Hydraulically Fractured Wells in Shale Plays: Examples from HFTS-1 (Hydraulic Fracture Test Site-1, Midland Basin, West Texas)

The present study shows how new Gaussian solutions of the pressure diffusion equation can be applied to model the pressure depletion of reservoirs produced with hydraulically multi-fractured well systems. Three practical application modes are discussed: (1) Gaussian decline curve analysis (DCA), (2) Gaussian pressure-transient analysis (PTA) and (3) Gaussian reservoir models (GRMs). The Gaussian DCA is a new history matching tool for production forecasting, which uses only one matching parameter and therefore is more practical than hyperbolic DCA methods. The Gaussian DCA was compared with the traditional Arps DCA through production analysis of 11 wells in the Wolfcamp Formation at Hydraulic Fracture Test Site-1 (HFTS-1). The hydraulic diffusivity of the reservoir region drained by the well system can be accurately estimated based on Gaussian DCA matches. Next, Gaussian PTA was used to infer the variation in effective fracture half-length of the hydraulic fractures in the HFTS-1 wells. Also included in this study is a brief example of how the full GRM solution can accurately track the fluid flow-paths in a reservoir and predict the consequent production rates of hydraulically fractured well systems. The GRM can model reservoir depletion and the associated well rates for single parent wells as well as for arrays of multiple parent–parent and parent–child wells.

Data-Driven Approach Develops Proactive Chemical Treatment Strategies

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 3723425, “Delaware Basin Formational Solid Deposit Trends: A Data-Driven Look at Developing Proactive Chemical Treatment Strategies,” by Rachel W. Hudson, SPE, and Kevin J. Spicka, SPE, ChampionX, and Ryan W. Pagel, SPE, Cooper Gray Consulting, et al. The paper has not been peer reviewed. _ This paper highlights a chemical-treatment strategy developed specifically for solids mitigation for a Delaware Basin operator. More than 200 Delaware Basin solid samples from the New Mexico/Texas state line were collected over a 2-year period, spanning seven distinct producing intervals. A statistical approach was taken to determine which formations had the greatest potential for paraffin, carbonate, acid-soluble iron compounds, and sulfate scales. A similar macroscopic approach in other basins may be applied to identify what proactive treatment strategies could be developed based upon the specific challenges of those regions and may similarly improve field performance. Introduction A need exists for optimized cost-effective solids-mitigation and treatment strategies for oil and gas operators across multiple basins. The key is knowing what type of solids have the greatest potential to form before they precipitate and cause issues within a given system. There is a long-standing history of successful chemical and mechanical treatment strategies for solids in the Midland Basin, but less is known about recent unconventional developments in the Delaware Basin, particularly in the Wolfcamp and Bone Springs formations. The Delaware Basin, located within the Permian, is a series of stacked plays. The Avalon sits above the First, Second, and Third Bone Springs, all of which are Leonard-age strata. These formations sit stratigraphically above the Wolfcamp formations. Each producing interval has similar characteristics that directly affect the risk of solid precipitation. During the last decade, the most-common unconventional targets in the Delaware Basin have been the Avalon, Bone Springs, and Wolfcamp formations, which are the focus of the complete paper. Designing cost-effective proactive chemical or mechanical treatment strategies and optimizing those efforts is a challenging dynamic process. Historically, operators take one of two approaches: treat everything while changing chemical type on an as-needed basis or only treating locations where a problem has occurred in the past. Theory and Methods From experiences recorded across the Delaware Basin, it is clear that some formations have more scale or paraffin issues, but this has all been based on individual field experience in localized areas. For example, it is not uncommon to hear that the Second Bone Springs tends to have paraffin issues. If this is true on a larger scale, then widespread untreated solids-deposit data should be able to indicate which solids are the most probable from each formation. If a pattern can be found in the data, then a proactive risk-mitigation and treatment strategy potentially could be defined by formation. If such a data trend exists, it could provide great value and simplify upfront treatment methodology.

Unsupervised time series clustering, class-based ensemble machine learning, and petrophysical modeling for predicting shear sonic wave slowness in heterogeneous rocks

Shear sonic logs are critical for formation evaluation, rock physics, quantitative reservoir characterization, and geomechanical studies. Although empirical and conventional machine learning (ML) have been used for shear sonic slowness estimates, both approaches suffer from multiple fundamental problems. New approaches to ML, namely, unsupervised multivariate time series clustering (Toeplitz inverse covariance-based clustering) and class-based ensemble ML, are integrated with geologic information and petrophysical inversion-based multimineral models to predict rock properties of the Wolfcamp Formation in the Midland Basin, United States. These new approaches consider the interdependence of wireline log attributes, temporal or depth dependence of log responses, multimodal geologic factors, and variability of individual ML model-based results to generate the best possible ML model. The results from the proposed approach indicate highly accurate and consistent shear sonic log predictions with a minimal error for thousands of feet (hundreds of meters) in several wells in the Wolfcamp Formation, covering a large study area in the basin. The results are used in estimating elastic properties, such as Poisson’s ratio, lambda-rho (incompressibility), and mu-rho (rigidity) to delineate brittle and ductile units in the formation.

An Efficient Model for Hydraulic Fracture Height Growth Considering the Effect of Bedding Layers in Unconventional Shale Formations

Summary Understanding and quantifying the effect of the weak bedding layers on the hydraulic fracture height growth in shale formations has always been challenging during the hydraulic fracturing design and operations. This work introduces a comprehensive and nonintrusive hydraulic fracture height growth model that accounts for both formation rock properties and the weak interfaces and can be incorporated into pseudo-3D hydraulic fracture simulators. The formation rock properties are handled by the equilibrium fracture height model. The shear slippage of the weak bedding layers is simulated by an efficient 2D higher-order displacement discontinuity method (HDDM), and the effectiveness on the hydraulic fracture height is quantified by correcting the stress intensity factors (SIFs) at the hydraulic fracture tips. The model is applied in the Permian Basin Wolfcamp formation to quantitatively investigate the effect of bedding layers on the hydraulic fracture height growth. Numerical studies show that the shear slippage of the beddings can significantly slow down the hydraulic fracture height growth and that the shear fracture toughness of the bedding layers and the spacing between the laminations have a considerable impact on the effectiveness of the shear slippage on the fracture height growth. Larger shear fracture toughness and smaller spacing of the bedding layers add more resistance to the fracture height propagation and require larger pressure to breakthrough. Additional results show that the effect of bedding layers is more significant in formations with low-stress contrast but less obvious in formations with high-stress contrast. The impact of the landing depth on the hydraulic fracture height growth is also investigated to provide crucial insights into the optimization of the landing depth in laminated formations.

A geochemical analysis of produced water(s) from the Wolfcamp formation in the Permian Delaware Basin, western Texas

A geochemical analysis of produced water(s) from the Wolfcamp formation in the Permian Delaware Basin, western Texas

Integrated detection and investigation of bad borehole section in the Wolfcamp Formation in the Midland Basin using machine learning, petrophysics, and core characterization

The quality of formation evaluation and reservoir estimates depends a lot on the quality of open hole logs, which is intricately tied to the borehole geometry and stand-offs. Bad borehole sections cause interesting patterns on wireline logs that can result in multiple interpretations. We have conducted an automated identification of bad borehole sections (>50 ft [15 m] thick) in multiple wells in the Wolfcamp Formation in the Midland Basin of the United States and investigated the causalities behind the unstable borehole section. The wells impacted by bad hole conditions appear to be within a particular region of the northern Midland Basin, and the bad hole condition impacts a specific zone in the Wolfcamp D. We consider well logs as multivariate time series (or depth series), where they share interdependencies to a large extent. We use this concept and unsupervised multivariate time series clustering to automatically identify bad borehole sections. Our proposed workflow simultaneously clusters the wireline logs, resulting in a large labeled regional-scale data set that can be further used for log quality assurance, processing, and petrophysical modeling. More importantly, we investigate the reasons behind the borehole washout, integrating sedimentology and geochemical data. Thick borehole washout sections are related to the presence of a significant amount of clay (>73 wt%, a mixture of smectite and illite) in the Wolfcamp D. The results also indicate that the manual editing of wireline logs and even supervised machine learning-based log reconstruction (commonly known as a missing log prediction problem) can lead to erroneous and inconsistent interpretation of subsurface characteristics. Such exercise can be meaningless if derived models are not validated with the ground truth (preserved subsurface rocks). Geologic feature: Clay-rich unstable borehole section Log appearance: Large-to-small caliper response associated with erratic density and neutron porosity logs Features with similar appearance: Gypsum, siderite nodules, and tool failure Formation: Wolfcamp Formation Age: Wolfcampian Location: Northern Midland Basin, Texas, USA Well data: Obtained from the IHS Analysis tools: Unsupervised machine learning, core description, geochemistry, and petrophysical inversion modeling

Application of a Deep Learning Network for Joint Prediction of Associated Fluid Production in Unconventional Hydrocarbon Development

Machine learning (ML) approaches have risen in popularity for use in many oil and gas (O&G) applications. Time series-based predictive forecasting of hydrocarbon production using deep learning ML strategies that can generalize temporal or sequence-based information within data is fast gaining traction. The recent emphasis on hydrocarbon production provides opportunities to explore the use of deep learning ML to other facets of O&G development where dynamic, temporal dependencies exist and that also hold implications to production forecasting. This study proposes a combination of supervised and unsupervised ML approaches as part of a framework for the joint prediction of produced water and natural gas volumes associated with oil production from unconventional reservoirs in a time series fashion. The study focuses on the pay zones within the Spraberry and Wolfcamp Formations of the Midland Basin in the U.S. The joint prediction model is based on a deep neural network architecture leveraging long short-term memory (LSTM) layers. Our model has the capability to both reproduce and forecast produced water and natural gas volumes for wells at monthly resolution and has demonstrated 91 percent joint prediction accuracy to held out testing data with little disparity noted in prediction performance between the training and test datasets. Additionally, model predictions replicate water and gas production profiles to wells in the test dataset, even for circumstances that include irregularities in production trends. We apply the model in tandem with an Arps decline model to generate cumulative first and five-year estimates for oil, gas, and water production outlooks at the well and basin-levels. Production outlook totals are influenced by well completion, decline curve, and spatial and reservoir attributes. These types of model-derived outlooks can aid operators in formulating management or remedial solutions for the volumes of fluids expected from unconventional O&G development.

Mechanisms for the Generation of Complex Fracture Networks: Observations From Slant Core, Analog Models, and Outcrop

We use observations of hydraulic fractures in core, outcrop attributes of natural hydraulic fractures, and analogue models, to address how hydraulic fracture networks evolve. A slant core from the Wolfcamp Formation—an unconventional shale hydrocarbon reservoir in the Permian Basin of West Texas—collected within 18 m and 30 m of two hydraulically stimulated horizontal wells, provided an opportunity to examine hydraulic fractures directly. In approximately 183 m of core, 309 calcite-sealed natural opening-mode fractures and 375 hydraulic fractures were identified. Many hydraulic fractures in the core show complex morphology, including twist-hackle segmentation, diversion, and bifurcation; these structures most commonly develop at lithological bed boundaries and mechanical heterogeneities such as natural fractures and concretions. An outcrop of bed-parallel pavements in the Cretaceous Boquillas Formation in West Texas contains opening-mode fractures that likely formed by natural hydraulic fracturing. Fracture traces provide evidence of twist-hackle segmentation, and are typically associated with bed boundaries and preexisting bed-parallel stylolites. A laboratory study of hydraulic fracturing of 33 synthetic blocks of gypsum and hydrostone revealed fracture steps, diversions, twist hackles, and multiple overlapping fractures together with information on fracture growth directions. These complexities in the fracture network were dominantly nucleated at inclusions used to simulate pre-existing fractures, and as a result of mechanical heterogeneity introduced by the wellbore and perforations. Collectively, our results show that complex fracture networks are produced in hydraulic fracturing of self-sourced reservoir strata. Mechanical stratigraphic boundaries and other heterogeneities are likely to enhance fracture network complexity through the processes of segmentation, diversion, and bifurcation. These processes create multiple fracture strands, resulting in an increased number of hydraulic fractures over those initiated, thereby increasing total fracture surface area. Our study provides insight into hydraulic fracture network propagation, and has applications for evaluation, completion, production, and fracture modeling of unconventional reservoirs.

Searching for the root cause of shale well rate variance: Highly variable fracture treatment response

This case study highlights that shale wells drilled with tight well spacing in the same landing zone using the same fracture treatment plan, commonly show large variance in well productivity. Searching for the root cause(s) of this variance in performance, we conclude that the factory model – which assumes wells can be engineered in reproducible and identical ways – is not yet feasible in practice. For each well, largely similar fracture treatment schedules result in different fracture half lengths. Also, a significant proportion of perforations may fail to create any hydraulic fractures at all. This new reality means that underperforming wells in otherwise promising acreage are more likely an indication of varying fracture treatment response, rather than a result of poor source rock. Research focusing on the reduction of fracture treatment variance therefore deserves priority and may help steady the performance of future wells. • Shale wells drilled with factory model, commonly show large variance in well productivity. • Root cause(s) of well performance variance is researched. • Varying fracture treatment response is the main cause of underperforming wells. • Wolfcamp Formation gas and water ratios as a function of production time are discussed. • Each of the principal field development choices is briefly reviewed.

Application of benchtop humidity and temperature chamber in the measurement of water vapor sorption in US shales from Mancos, Marcellus, Eagle Ford and Wolfcamp formations

A benchtop humidity and temperature chamber was used to assess water vapor sorption in four US shale samples at 90 °C. Water sorption isotherms were measured at relative humidity ranging from 10 to 99% and temperature of 90 °C. Shale fractal properties were then evaluated, and capillary pressure (ranging from 1.70 to 386 MPa) was obtained using Kelvin relationship. The results show that Mancos shale, from the US, adsorbed more absorbed water due to its high clay concentration and low TOC. However, Wolfcamp shale, from the US, has the lowest TOC and clay concentration, adsorbing the lowest amount of water. There is little hysteresis between adsorption and desorption isotherms explaining water retention phenomenon in some shales. The obtained fractal dimension values ranged between 2.45 and 2.76 and average of 2.56 indicating irregular pore surface and complex pore structure. All shale sample's capillary curves were fitted to Brooks & Corey and van Genuchten models with nonlinear regression. The fitting coefficient, R2, which represents the proportion of variance for Brooks & Corey fits ranged from 0.90 to 0.97 for imbibition and 0.85 to 0.98 for drainage, while R2 for the van Genuchten model ranged from 0.94 to 0.99 for both imbibition and drainage. Thus, the proposed method can be used to measure capillary pressure–saturation relationships in gas shales.

Investigating Delaware Basin Bone Spring and Wolfcamp Observations Through Core-Based Quantification: Case Study in the Integrated Workflow, Including Closed Retort Comparisons

The Delaware Basin of the greater Permian Basin system has been the focus of continually increased attention in the exploration, appraisal, and development phases of unconventional oil and gas potential over the past decade. While the industry continues to drill horizontal wells for the exploitation of producible hydrocarbon, subsurface disciplines continue to investigate the rock and fluid properties of the stratigraphy through the application of various technological tools. In this study, we focus on five wells spatially covering Loving, Ward, and Reeves Counties in West Texas, where whole core samples were acquired and investigated to compare variations in the Bone Spring and Wolfcamp Formations across varying geological contexts. Samples taken from the whole conventional core were investigated in the laboratory setting, and a series of measurements were acquired on each sample. The laboratory measurements distinguished trends and changes in the rock property volumes (i.e., porosity, saturation, total organic carbon) over stratigraphic intervals. The utilization of nuclear magnetic resonance, as well as acquired geochemical data, allows an innovative approach and application of a correction factor to be applied to the saturation quantification. Integration of geological context with measured laboratory data constraints and petrophysical wireline-log-based interpretation links predictive trends from the defined rock and fluid property distributions and may aid in predicting hydrocarbon vs. water production.

Predicting variations of the least principal stress with depth: Application to unconventional oil and gas reservoirs using a log-based viscoelastic stress relaxation model

Knowledge of layer-to-layer variations of the least principal stress, S hmin, with depth is essential for optimization of multi-stage hydraulic fracturing in unconventional reservoirs. Utilizing a geomechanical model based on viscoelastic stress relaxation in relatively clay rich rocks, we present a new method for predicting continuous S hmin variations with depth. The method utilizes geophysical log data and S hmin measurements from routine diagnostic fracture injection tests (DFITs) at several depths for calibration. We consider a case study in the Wolfcamp formation in the Midland Basin, where both geophysical logs and values of S hmin from DFITs are available. We compute a continuous stress profile as a function of the well logs that fits all of the DFITs well. We utilized several machine learning technologies, such as bootstrap aggregation (or bagging), to improve the generalization of the model and demonstrate that the excellent fit between predicted and observed stress values is not the result of over-fitting the calibration points. The model is then validated by accurately predicting hold-out stress measurements from four wells within the study area and, without recalibration, accurately predicting stress as a function of depth in an offset pad about 6 miles away.

Productivity Prediction in the Upper Wolfcamp Formation Using Weighted Voting Ensemble Machine Learning Method

Productivity Prediction in the Upper Wolfcamp Formation Using Weighted Voting Ensemble Machine Learning Method

Quantification of mineral reactivity using machine learning interpretation of micro-XRF data

Accurate characterizations of mineral reactivity require mapping of spatial heterogeneity, and quantifications of mineral abundances, elemental content, and mineral accessibility. Reactive transport models require such information at the grain-scale to accurately simulate coupled processes of mineral reactions, aqueous solution speciation, and mass transport. In this work, millimeter-scale mineral maps are generated using a neural network approach for 2D mineral mapping based on synchrotron micro x-ray fluorescence (μXRF) data. The approach is called Synchrotron-based Machine learning Approach for RasTer (SMART) mapping, which reads μXRF scans and provides mineral maps of the same size and resolution. The SMART mineral classifier is trained on coupled μXRF and micro-x-ray diffraction (μXRD) data, which is what distinguishes it from existing mapping tools. Here, the SMART classifier was applied to μXRF scans of various sedimentary rock samples including consolidated shales from the Eagle Ford (EFS1), Green River (GRS1), Haynesville (HS1), and New Albany (NAS1) formations and a syntaxial vein from the Upper Wolfcamp formation. The data were obtained using an x-ray microprobe at beamline 13-ID-E at the Advanced Photon Sources. Individual mineral maps generated by the SMART classifier well-captured distributions of both dominant and minor phases in the shale rocks and revealed EFS1 and GRS1 to be carbonate rich shales, and NAS1 and HS1 to be sulfide rich shales. The EFS1 was further characterized for its trace mineral abundances, grain sizes, trace element composition, and accessibility. Approximately 4.4 wt% of the rock matrix were found to be pyrite, with a median grain size of 3.17 μm in diameter and 62% of the grains predicted to be smaller than 4 μm. Quantifications of trace elements in pyrite revealed zinc concentrations up to 4.2 wt%, along with minor copper and arsenic copresence. Mineral accessibility was examined by contact with other phases and was quantified using a new type of image we are calling an adjacency map. Adjacency analyses revealed that of the total pyrite surface present in the EFS1, 28% is in contact with calcite. The adjacency maps are useful for quantifying the likelihood that a mineral could be exposed to fluids after dissolution of a contacting reactive phase like calcite. Lastly, pooling data from different samples was demonstrated by training a classifier using two sets of coupled μXRF-μXRD data. This classifier yielded an overall accuracy of >96%, demonstrating that data pooling is a promising approach for applications to a wide suite of rock samples of different origin, size, and thickness.

Enhanced Assessment of Fluid Saturation in the Wolfcamp Formation of the Permian Basin

Conventional resistivity models often overestimate water saturation in organic-rich mudrocks and require extensive calibration efforts. Conventional resistivity-porosity-saturation models assume brine in the formation as the only conductive component contributing to resistivity measurements. They also do not reliably assimilate the spatial distribution of the clay network and pore structure. Moreover, they do not incorporate other conductive minerals and organic matter, impacting the resistivity measurements and leading to uncertainty in water saturation assessment. We recently introduced a resistivity-based model that quantitatively assimilates the type and spatial distribution of all rock constituents to improve reserves evaluation in organic-rich mudrocks using electrical resistivity measurements. This paper aims to expand the application of this model for well-log-based assessment of water/hydrocarbon saturation and to verify the reliability of the introduced method in the Wolfcamp Formation of the Permian Basin. Our recently introduced resistivity model uses pore combination modeling to incorporate conductive (clay, pyrite, kerogen, brine) and nonconductive (grains, hydrocarbon) components in estimating effective resistivity. The inputs to the model are volumetric concentrations of minerals, conductivity of rock components, and porosity obtained from laboratory measurements or interpretation of well logs. Geometric model parameters are also critical inputs to the model. To simultaneously estimate the geometric model parameters and water saturation, we developed an inversion algorithm with two objectives: (a) to estimate the geometric model parameters as inputs to the new resistivity model and (b) to estimate the water saturation. The geometric model parameters are determined for each rock type or formation by minimizing the difference between the measured resistivity and the resistivity estimated from pore combination modeling. We applied the new method to two wells drilled in the Wolfcamp Formation of the Permian Basin. The formation-based inversion showed variation in geometric model parameters, which improved the assessment of water saturation. Results demonstrated that the new method improved water saturation estimates by 24.1% and 32.4% compared to Archie’s and Waxman-Smits models, respectively, in the Wolfcamp Formation. The most considerable improvement was observed in the Middle and the Lower Wolfcamp Formations, where the average clay concentration was relatively higher than the other zones. There was an additional 70,000 bbl/acre of hydrocarbon reserve using the proposed method compared to when water saturation was quantified using Archie’s model in the Permian Basin, which is a 33% relative improvement. It should be highlighted that the new method did not require any calibration effort using core water saturation measurements, which is a unique contribution of this rock-physics-based workflow.

A Two-Phase Flow Model for Reserves Estimation in Tight-Oil and Gas-Condensate Reservoirs Using Scaling Principles

Summary A common approach to forecast production from unconventional reservoirs is to extrapolate single-phase flow solutions. This approach ignores the effects of multiphase flow, which exist once the reservoir pressure falls below the bubble/dewpoint. This work introduces a new two-phase (oil and gas) flow solution suitable to extrapolating oil and gas production using scaling principles. In addition, this study compares the application of the two-phase and the single-phase solutions to estimates of production from tight-oil wells in the Wolfcamp Formation of west Texas. First, we combine the oil and the gas flow equations into a single two-phase flow equation. Second, we introduce a two-phase pseudopressure to help linearize the pressure diffusivity equation. Third, we cast the two-phase diffusion equation into a dimensionless form using inspectional analysis. The output of the model is a predicted dimensionless flow rate that can be easily scaled using two parameters: a hydrocarbon pore volume and a characteristic time. This study validates the solution against results of a commercial simulator. We also compare the results of both the two-phase and the single-phase solutions to forecast wells. The results of this research are the following: First, we show that single-phase flow solutions will consistently underestimate the oil ultimate recovery factors (URFs) for solution gas drives. The degree of underestimation will depend on the reservoir and flowing conditions as well as the fluid properties. Second, this work presents a sensitivity analysis of the pressure/volume/temperature (PVT) properties, which shows that lighter oils (more volatile) will yield larger recovery factors for the same drawdown conditions. Third, we compare the estimated ultimate recovery (EUR) predictions for two-phase and single-phase solutions under boundary-dominated flow (BDF) conditions. The results show that single-phase flow solutions will underestimate the ultimate cumulative oil production of wells because they do not account for liberation of dissolved gas and its subsequent expansion (pressure support) as the reservoir pressure falls below the bubblepoint. Finally, the application of the two-phase model provides a better fit when compared with the single-phase solution. The present model requires very little computation time to forecast production because it only uses two fitting parameters. It provides more realistic estimates of URFs and EURs, when compared with single-phase flow solutions, because it considers the expansion of the oil and gas phases for saturated flow. Finally, the solution is flexible and can be applied to forecast both tight-oil and gas condensate wells.

Effects of Overpressure on Mechanical Properties of Unconventional Shale Reservoirs through Novel Use of a Sonic Overpressure Indicator

Summary Overpressure is a common feature among productive unconventional shale reservoirs, such as the Bone Spring (BSPG) and Wolfcamp (WFMP) Formations of the Delaware Basin (DB) of west Texas and southeastern New Mexico, and is thought to be a strong driver of well productivity. Compared with conventional reservoirs and shales in normal pressured conditions, the effects of overpressure on the mechanical properties of shales is not well understood. Here we present an analysis of overpressure in clay-bearing siliciclastic facies of the BSPG and WFMP Formations of the DB and implications for mechanical properties of the reservoir. Estimation of the effects of overpressure on mechanical properties of unconventional shale reservoirs is determined through use of the sonic overpressure indicator (SOPI). The method requires log model results that accurately characterize variations in lithology and porosity for the formations of interest. The SOPI (ΔT/ΔTN)2, where ΔT is the measured compressional sonic transit time, and ΔTN is the forward-modeled result for normally pressured conditions, can be used with elastic moduli and their interrelationships to compare estimates of mechanical properties including Poisson’s ratio ν, the Biot or effective stress coefficient α, and Young’s modulus E, in normal and overpressured conditions. Results presented here are broadly applicable to overpressured unconventional reservoirs that contain significant clay volume (>0.1 v/v) and exhibit low porosity (<0.08 v/v), comparable to that of siliciclastic-rich facies of the WFMP Formation. To account for increased VP/VS ratio, we regard overpressurization of shaly facies as an irreversible thermodynamic process that transforms a normally pressured siliciclastic system. At stress below the yield point, which is taken as the limit of normal pressure, the system responds elastically to stress; beyond this point, during overpressurization, the system responds as an elastic/plastic medium with strain hardening. We regard elastic moduli as descriptive of mechanical energy stored in this system. This perspective enables Poisson’s ratio for the overpressured system νOP to be computed from an estimate of the normally pressured system νN using (ΔT/ΔTN)2. Overpressure also results in a limited increase of the Biot or effective stress coefficient α. Moreover, recognition that overpressure results in a decrease of Young’s modulus, that is, EOP/EN < 1, provides a means of estimating the amount of strain energy stored by the formation due to overpressurization. We believe that when exposed to lower pressures by wellbore construction, this strain energy stored in overpressured unconventional reservoirs drives creep, which affects interpretations made using geomechanical models. We have developed and tested computational models based on biaxial or plane strain for vertical wells and uniaxial strain for horizontal wells that describe how creep likely affects estimation of minimum horizontal stress Shmin and pore pressure from instantaneous shut-in-pressure (ISIP) measurements. Thus, for overpressured unconventional reservoirs, ISIP determinations differ from tectonic Shmin by an amount related to ν and EOP/EN.