This study presents a novel metaheuristic algorithm that uses a physics-based model for multi-fractured horizontal wells (MFHW) to accurately predict the estimated ultimate recovery (EUR) for unconventional reservoirs. The metaheuristic algorithm creates a sizeable number of stochastic simulations and keeps the simulation results from those random models that closely reproduce observed production data. Unlike other optimization methods, the proposed algorithm does not aim at finding the exact solution to the problem but a group of sufficiently accurate solutions that help to construct the partial solution to the optimization problem as a function of production history. Results from this work provide sufficient evidence as to why traditional decline curve analysis (DCA) is not a suitable solution for production forecasting in unconventional reservoirs. Two case studies are discussed in this work where results from both modeling strategies are compared. Evolutionary prediction of EUR over time using DCA behaves erratically, regardless of the amount of historical production data available to the regression model. Such erratic behavior can, in turn, yield an erroneous estimation of key economic performance indicators of an asset. In contrast, the proposed metaheuristic algorithm delivers precise and accurate results consistently, achieving a significant reduction of uncertainties as more production data becomes available. In conclusion, the proposed partial optimization approach enables the accurate calculation of important metrics for unconventional reservoirs, including production forecasting and expected productive life of an asset.

This paper presents a continuum-scale diffusion-based model informed by pore-scale data for gas transport in organic nanoporous media. A mass transfer and adsorption model is developed by considering multiple transport and storage mechanisms, including bulk diffusion and Knudsen diffusion for free phase, surface diffusion for sorbed phase, and multilayer adsorption. The continuum-scale diffusion-based governing equation is developed solely based on free phase concentration for the overall mass conservation of free and sorbed phases, carrying a newly-defined effective diffusion coefficient and a capacity factor to account for multilayer adsorption. Diffusion of free and sorbed phases is coupled through the pore-scale simplified local density method based on the modified Peng-Robinson equation of state for confinement effects. The model is first utilized to analyze pore-scale adsorption data from the krypton (Kr) gas adsorption experiment on graphite. Then we implement the model to conduct sensitivity analysis for the effects of pore size on gas transport for Kr-graphite and methane-coal systems. The model is finally used to study Kr diffusion profiles through a coal matrix obtained through X-ray micro-CT imaging. The results show that the sorbed phase occupies most of the pore space in organic nanoporous media due to multilayer adsorption, and surface diffusion contributes significantly to the total mass flux. Therefore, neglecting the volume of sorbed phase and surface diffusion in organic nanoporous rocks may result in considerable errors. Furthermore, the results reveal that implementing a Langmuir-based model may be erroneous for an organic-rich reservoir with nanopores during the early depletion period when the reservoir pressure is high.

Hydraulic fracture/reservoir properties and fluid-in-place can be quantified by using rate-transient analysis (RTA) techniques applied to flow rates/pressures gathered from multi-fractured horizontal wells (MFHWs) completed in unconventional reservoirs. These methods are commonly developed for the analysis of production data from single wells without considering communication with nearby wells. However, in practice, wells drilled from the same pad can be in strong hydraulic communication with each other. This study aims to develop the theoretical basis for analyzing production data from communicating MFHWs completed in single-phase shale gas reservoirs. A simple and practical semi-analytical method is developed to quantify the communication between wells drilled from the same pad by analyzing online production data from the individual wells. This method is based on the communicating tanks model and employs the concepts of macroscopic material balance and the succession of pseudo-steady states. A set of nonlinear ordinary differential equations (ODEs) are generated and solved simultaneously using the efficient Adams-Bashforth-Moulton algorithm. The accuracy of the solutions is verified against robust numerical simulation. In the first example provided, a MFHW well-pair is presented where the wells are communicating through primary hydraulic fractures with different communication strengths. In the subsequent examples, the method is extended to consider production data from a three-well and a six-well pad with wine-rack-style completions. The developed model is flexible enough to account for asynchronous wells that are producing from distinct reservoir blocks with different fracture/rock properties. For all the studied cases, the semi-analytical method closely reproduces the results of fully numerical simulation. The results demonstrate that, in some cases, when new wells start to produce, the production rates of existing wells can drop significantly. The amount of productivity loss is a direct function of the communication strengths between the wells. The new method can accurately quantify the communication strength between wells through transmissibility multipliers between the hydraulic fractures that are adjusted to match individual well production data. In this study, a new simple and efficient semi-analytical method is presented that can be used to analyze online production data from multiple wells drilled from a pad simultaneously with minimal computation time. The main advantage of the developed method is its scalability, where additional wells can be added to the system very easily.

Image-based characterization of rock fabric is critical for understanding recovery mechanisms in shale formations due to the significant multiscale nature of shale source rocks. Nanoscale imaging is particularly important for characterizing pore-scale structure of shales. Nanoimaging techniques, however, have a tradeoff between high-resolution/high-contrast sample-destructive imaging modalities and low-contrast/low-resolution sample-preserving modalities. Furthermore, acquisition of nanoscale images is often time-consuming, expensive, and requires signficant levels of expertise, resulting in small image datasets that do not allow for accurate quantification of petrophysical or morphological properties. In this work, we introduce methods for overcoming these challenges in image-based characterization of the fabric of shale source rocks using deep learning models. We present a multimodal/multiscale imaging and characterization workflow for enhancing non-destructive microscopy images of shale. We develop training methods for predicting 3D image volumes from 2D training data and simulate flow through the predicted shale volumes. We then present a novel method for synthesizing porous media images using generative flow models. We apply this method to several datasets, including grayscale and multimodal 3D image volume generation from 2D training images. Results from this work show that the proposed image reconstruction and generation approaches produce realistic pore-scale 3D volumes of shale source rocks even when only 2D image data is available. The models proposed here enable new capabilities for non-destructive imaging of source rocks and we hope will improve our ability to characterize pore-scale properties and phenomena in shales using image data.

Hydraulic fracturing stimulation of unconventional wells employing large volumes of sand in low viscosity fluids provides propped fracture conductivity in less than 25% of the created fracture area, primarily because of poor sand transport mechanics. The remaining unpropped area is at best only marginally productive using the conventional sand/slickwater hydraulic fracturing process alone. Near-neutrally buoyant proppants (NBPs, ASG 1.055) have proven to be highly effective in accessing production from fracture area that is otherwise left unpropped. Fracture models illustrate the propped fracture area of designs incorporating NBPs is improved to over 85% of the created fracture area. Production simulations of typical slickwater and sand frac designs supplemented with NBPs at 3% by weight of sand distributed evenly throughout the slurry stages show cumulative production increases of 20% to greater than 50% compared to the large volume slickwater/sand treatments without NBPs. Efforts have been directed to justification of the incremental expense involved with the NBP applications and assessment of the associated value-added economic metrics, including the value of the realized incremental production vs. time, the payback time for recovery of the incremental costs, and Return on Investment (ROI). For example, in a 2018 trial of NBP wells in the Middle Bakken formation of North Dakota, the production uplift observed for NBP wells achieved payback of the incremental costs in an average of 26 days; the 1-year cumulative oil production of the NBP wells averaged 69,632 barrels greater than control wells, resulting in a 25% uplift compared to the offset control wells. The Year 1 Return on Investment (ROI) for the drilling and completion costs of the first Middle Bakken well with NBP was 97% versus 64% for the sand-only control wells. Controlled multi-stage horizontal completions of wells with sand-only have been evaluated against wells utilizing NBPs in the application have been executed within several unconventional plays, including the Permian and Williston basins. The performance of the NBP wells have consistently validated the production uplift predictions of the production simulation models. The case studies analyzed herein expand the economic assessment of the NBP stimulation designs by including production analyses quantitative comparison of Net Present Value, production decline rates, and projected EURs of the NBP wells and non-NBP offset wells.

There are increased development activities in shale reservoirs with ultra-low permeability thanks to the advances in drilling and fracking technology. However, representative reservoir fluid samples are still difficult to acquire. The challenge leads to limited reservoir fluid data and large uncertainties for shale play evaluation, field development, and production optimization. In this work, we built a large unconventional reservoir fluid database with more than 2400 samples from shale reservoirs in Canada, Argentina, and the USA, comprising early production surface gas data and traditional PVT data from selected shale assets. A machine learning approach was applied to the database to predict gas to oil ratio (GOR) in shale reservoirs. To enhance regional correlations and obtain a more accurate GOR prediction, we developed a machine learning model focused on Canada shale plays data, intended for wells with limited reservoir fluid data available and located within the same region. Both surface gas compositional data and well location and are input features to this model. In addition, we developed an additional machine learning model for the objective of a generic GOR prediction model without shale dependency. The database includes Canada shale data and Argentina and USA shale data. The GOR predictions obtained from both models are good. The machine learning model circumscribed to the Canada shale reservoirs has a mean percentage error (MAPE) of 4.31. In contrast, the generic machine learning model, which includes additional data from Argentina and USA shale assets, has a MAPE of 4.86. The better accuracy of the circumscribed Canada model is due to the introduction of the geospatial well location to the model features. This study confirms that early production surface gas data can be used to predict well GOR in shale reservoirs, providing an economical alternative for the sampling challenges during early field development. Furthermore, the GOR prediction offers access to a complete set of reservoir fluid properties which assists the decision-making process for shale play evaluation, completion concept selection, and production optimization.

Gas production from unconventional shale reservoirs is known for rapid declines. Intermittent shut-in production constitutes a technique typically applied to low-production wells during late life stages to maintain economic rates. This technique involves a cyclic process of shutting in the well temporarily to allow it to build up pressure and subsequently switching the well to production. Operators often manage hundreds of wells on intermittent shut-in production; these wells, however, incur different shut-in and production cycle times, thus requiring a complicated management approach. Because every well has a unique production behavior and reservoir characteristics, searching for optimum operational conditions individually is not only technically challenging, but also operationally time-consuming and labor- intensive. Our goal was to use active learning analytic, a type of machine learning deployed on an edge computing platform, to autonomously control and optimize these unconventional gas wells. The field trial results show increased production, reduced liquid loading, decreased manual intervention, and reduced carbon footprint. Our solution utilizes an edge computing platform to deploy the analytic on the wellhead without requiring a stable internet connection. A computing device at the edge connects to controllers on site, processes data, sets system control parameters, and enables automation for operations deploying an optimization algorithm. Active learning algorithms are valuable for use in the optimization of systems that are not mathematically definable. These algorithms are also proven to learn the relationship between the inputs and outputs and use prior knowledge to intelligently search for the optimum settings within the defined operating limits. The low latency of edge computing allows for high-frequency data collection in seconds and a rapid control of the wells. The edge device continuously monitors production and initiates re- optimization as needed when operational conditions change. We developed an analytic that autonomously controls the intermittent production technique where a well is shut-in based on a specified minimum gas production rate and opened when the pressure builds up to the specified target during the shut-in period. The analytic actively learns and measures the ways in which the specified parameters improve production rates. Additionally, the analytic continuously monitors production data and identifies any well liquid loading events. When liquid loading occurs in the wellbore as observed from the production pattern, the analytic automatically shuts in the well to build up pressure and minimizes additional liquid formation. In the field trial, we deployed the edge analytic to monitor gas production and the specified well shut-in and open conditions for 10 different wells in the Haynesville Shale Play. Analyzing each well in the context of approximately 30 intermittent production cycles (shut- in/open), the analytic successfully mapped the surface response, identified the optimal setting for well shut-in/open conditions, and continuously updated the surface response. Overall, the analytic improved production by 4% and reduced the liquid loading occurrences and manual well unloading events by 94%, resulting in an average reduction of approximately 600 tons of CO 2 equivalent per well per year. In summary the active learning analytic was developed and deployed on an edge computing platform to 1) optimize intermittent shut-in by searching for the optimum settings that yield the most gas production; 2) automate the optimization process; and 3) monitor the liquid formation for potential loading events. In this paper, we present a use case for an algorithm adapted for the optimization of a dynamic system such as hydrocarbon production from a well.

The mechanical and petrophysical behavior of organic-rich carbonates (ORC) is affected significantly by burial diagenesis and the thermal maturation of their organic matter. Therefore, establishing Rock Physics (RP) relations and appropriate models can be valuable in delineating the spatial distribution of key rock properties such as the total organic carbon (TOC), porosity, water saturation, and thermal maturity in the petroleum system. These key rock properties are of most importance to evaluate during hydrocarbon exploration and production operations when establishing a detailed subsurface model is critical. High-resolution reservoir models are typically based on the inversion of seismic data to calculate the seismic layer properties such as P- and S-wave impedances (or velocities), density, Poisson's ratio, Vp/Vs ratio, etc. If velocity anisotropy data are also available, then another layer of data can be used as input for the subsurface model leading to a better understanding of the geological section. The challenge is to establish reliable geostatistical relations between these seismic layer measurements and petrophysical/geomechanical properties using well logs and laboratory measurements. In this study, we developed RP models to predict the organic richness (TOC of 1-15 wt%), porosity (7-35 %), water saturation, and thermal maturity (Tmax of 420-435⁰C) of the organic-rich carbonate sections using well logs and laboratory core measurements derived from the Ness 5 well drilled in the Golan Basin (950-1350 m). The RP models are based primarily on the modified lower Hashin-Shtrikman bounds (MLHS) and Gassmann's fluid substitution equations. These organic-rich carbonate sections are unique in their relatively low burial diagenetic stage characterized by a wide range of porosity which decreases with depth, and thermal maturation which increases with depth (from immature up to the oil window). As confirmation of the method, the levels of organic content and maturity were confirmed using Rock-Eval pyrolysis data. Following the RP analysis, horizontal (HTI) and vertical (VTI) S-wave velocity anisotropy were analyzed using cross-dipole shear well logs (based on Stoneley waves response). It was found that anisotropy, in addition to the RP analysis, can assist in delineating the organic-rich sections, microfractures, and changes in gas saturation due to thermal maturation. Specifically, increasing thermal maturation enhances VTI and azimuthal HTI S-wave velocity anisotropies, in the ductile and brittle sections, respectively. The observed relationships are quite robust based on the high-quality laboratory and log data. However, our conclusions may be limited to the early stages of maturation and burial diagenesis, as at higher maturation and diagenesis the changes in physical properties can vary significantly.

The advancements in production technologies have unlocked tremendous reserves of natural gas in shale formations. The ability to describe shale matrix dynamics during the production span is, however, at infancy stages. The complex mineralogy and the multiscale nature of shales require transport models beyond the classical Darcian framework. Shales primarily consist of clays, quartz, calcite, and some fragments of organic matters known as kerogen. The latter can be envisioned as naturally occurring nanoporous media where diffusion is believed to be the predominant transport mechanism. Moreover, kerogen exhibits different geo-mechanical behavior than typical clastic sedimentary rocks. Hence, kerogen responds to changes in the stress field differently during the production span and ultimately influences the transport. It is our aim in this paper to delineate the transport and geo-mechanical aspects of kerogen through molecular-based assessments. Realistic kerogen structures at some ranges of density were recreated on a computational platform for thorough investigations. The structures were analyzed for porosity, pore size distribution, and mechanical properties such as bulk modulus, shear modulus, Young's modulus, and Poisson ratio. The adsorption alongside self-diffusion calculations were performed on the configurations. Moreover, the assessment of diffusivity was linked to pore compressibility to address the impact of effective stress changes on the transport throughout typical production span. An effective diffusion model for kerogen was proposed, validated with molecular simulation data in the literature, and compared with the MD diffusion data of this study. The results revealed critical dependency of pore size distribution, and porosity on the effective stress, which severely alters the diffusive permeability. This work provides a novel methodology for linking kerogen microscale intricacies to some fundamental transport and mechanical properties to better describe the transport of natural gas from kerogen.

Volcanic ash beds are thin layers commonly observed in the Eagle Ford, Niobrara and, Vaca Muerta formations. Because of their differences in composition, sedimentary structures, and diagenetic alteration, they exhibit a significant contrast in mechanical properties with respect to surrounding formation layers. This can impact hydraulic fracturing, affecting fracture propagation and fracture geometry. Quantifying the mechanical properties of ash beds becomes significant; however, it is a challenge with traditional testing methods. Common logging fails to identify the ash beds, and core plug testing is not possible because of their friability. In this study, nanoindentation was used to measure the mechanical properties (Young's modulus, creep, and anisotropy) in Eagle Ford ash beds, and to determine the contrast with the formation matrix properties. Two separate ash beds of high clay and plagioclase composition were epoxied in an aluminum tray and left for 48 hours curing time. Horizontal and vertical samples of ash beds were acquired and mounted on a metal stub, followed by polishing and broad beam ion milling. Adjacent samples were also prepared for high-resolution Scanning Electron Microscope (SEM) microstructural analysis. The Young's modulus in ash beds ranged from 12 to 24 GPa, with the horizontal direction Young's modulus being slightly greater than that of the vertical samples. The Young's modulus contrast with adjacent layers was calculated to be 1:2 with clay-rich zones and 1:4 with calcite rich zones. The creep deformation rate was three times higher for ash beds compared to other zones. Using Backus averaging, it was determined that the presence of ash beds can increase the anisotropy in the formation by 15-25%. SEM results showed a variation in microstructure between the ash beds with evidence of diagenetic conversion of rhyolitic material into clays. Key differences between the two ash beds were due to the presence of plagioclase and the occurrence of porosity within kaolinite. Overall porosity varied between the two ash beds and adjacent carbonate layers showing a significant increase in porosity. Understanding the moduli contrast between adjacent layers can improve the hydraulic fracturing design when ash beds are encountered. In addition, the presence of these beds can lead to proppant embedment and loss in fracture connectivity. These results can be used for improving geomechanical models.

Field evidence of production logs after fracturing have documented the existence of abundant natural fractures in Weiyuan shale plays, which is widely acknowledged to have a positive impact on fracture network complexity. On the other hand, cases of severe casing failures have been frequently reported in this field during multistage fracturing jobs. Stress interference between two adjacent stages may intensify non-uniform loading on the casing string and accommodate failure. To better understand this problem, we establish a coupled 3D reservoir-scale model with complex well trajectory and tie it to a single well-scale model consisting of casing and the surrounding cement sheath. Using this model, we investigate the potential impacts of cement deficiency, clustered perforations, fracture geometry as well as spacing strategy on casing integrity. Our simulation results indicate that cement deficiency could intensify the load nonuniformity around the borehole which escalates the potential threats for casing failure. When cement deficiency reaches 45° along the minimum horizontal stress, it has the largest influence on the stress level in the casing. In addition, perforations could lower the casing strength, but the reduction may not change furthermore when the perforation diameter reaches a certain value. Moreover, impacts of fracture geometry and spacing on casing deformation are investigated. We conclude that the lower ratio of fracture length to its width and reasonable spacing strategy could help reduce the load non-uniformity on casing which avoid the casing deformation. The described workflow may be adopted in other areas to predict the possible casing failure problems induced by multistage hydraulic fracturing with cheap computational costs, to anticipate the challenges and avoid them by revisiting pumping schedule or spacing strategy.

Previously, it was shown that zeta potential could be used as a metric to determine friction reducer (FR) performance. Specifically, the extent of and how quickly the FR reaches peak friction reduction in source water. A correlation postulated from the previous work is zeta potentials relationship to an FR's stability during mechanical or chemical degradation. In other words, can zeta potential be used as a metric to determine the extent of polymer breaking and can this relationship be translated to regained conductivity? This paper describes a laboratory study of zeta potential measurements to track breaker reaction rates, stability of broken polymer dispersions, and the relationship between chemical degradation of FRs and regained conductivity. The approach of this investigation involves measuring zeta potential of frac fluids formulated using anionic and cationic FRs with varying types and concentrations of breakers at different temperatures and times. These metrics are then correlated with regain conductivity. A quantitative relationship exists between zeta potential, fluid rheology, and regain conductivity. Zeta potential evaluation of degraded FR's in frac fluids correlate to performance in regain conductivity testing. These measurements can expedite the selection of chemical breakers with respect to performance. Zeta potential measurements of degraded FR are indicative of broken FR dispersion stability which has impact on regain conductivity. Tracking behavior of cationic FR's using zeta potential reveals the materials can become anionic with time and temperature and become susceptible to agglomeration with iron. Zeta potential measurements can be used during a chemical breaker selection process as a viable supplement to industry standard tests for assessing the comparative effectiveness of chemical breakers in frac fluids.

Unconventional reservoir development is a multidisciplinary challenge due to complicated physical system, including but not limited to complicated flow mechanism, multiple porosity system, heterogeneous subsurface rock and minerals, well interference, and fluid-rock interaction. With enough well data, physics-based models can be supplemented with data driven methods to describe a reservoir system and accurately predict well performance. This study uses a data driven approach to tackle the field development problem in the Eagle Ford Shale. A large amount of data spanning major oil and gas disciplines was collected and interrogated from around 300 wells in the area of interest. The data driven workflow consists of: Descriptive model to regress on existing wells with the selected well features and provide insight on feature importance, Predictive model to forecast well performance, and Subject matter expert driven prescriptive model to optimize future well design for well economics improvement. To evaluate initial well economics, 365 consecutive days of production oil per CAPEX dollar spent (bbl/$) was setup as the objective function. After a careful model selection, Random Forest (RF) shows the best accuracy with the given dataset, and Differential Evolution (DE) was used for optimization. Using recursive feature elimination (RFE), the final master dataset was reduced to 50 parameters to feed into the machine learning model. After hyperparameter tuning, reasonable regression accuracy was achieved by the Random Forest algorithm, where correlation coefficient (R2) for the training and test dataset was 0.83, and mean absolute error percentage (MAEP) was less than 20%. The model also reveals that the well performance is highly dependent on a good combination of variables spanning geology, drilling, completions, production and reservoir. Completion year has one of the highest feature importance, indicating the improvement of operation and design efficiency and the fluctuation of service cost. Moreover, lateral rate of penetration (ROP) was always amongst the top two important parameters most likely because it impacts the drilling cost significantly. With subject matter experts’ (SME) input, optimization using the regression model was performed in an iterative manner with the chosen parameters and using reasonable upper and lower bounds. Compared to the best existing wells in the vicinity, the optimized well design shows a potential improvement on bbl/$ by approximately 38%. This paper introduces an integrated data driven solution to optimize unconventional development strategy. Comparing to conventional analytical and numerical methods, machine learning model is able to handle large multidimensional dataset and provide actionable recommendations with a much faster turnaround. In the course of field development, the model accuracy can be dynamically improved by including more data collected from new wells.

A critical component of natural gas in organic-rich shales is adsorbed gas within organic matter. Quantification of adsorbed gas is essential for reliable estimates of gas-in-place in shale reservoirs. However, conventional high-pressure adsorption measurements for coal on the volumetric method are prone to error when applied to characterize sorption kinetics in shale-gas systems due to limited adsorption capacity and finer pores of shale matrix. An innovated laboratory apparatus and measurement procedures have been developed for accurate determination of the relatively small amount of adsorbed gas in the Marcellus shale sample. The custom-built volumetric apparatus is a differential unit composed of two identical single-sided units (one blank and one adsorption side) connected with a differential pressure transducer. The scale of the differential pressure transducer is ± 50 psi, a hundred-fold smaller than the absolute pressure transducer measuring to 5000 psi, leading to a significant increase in the accuracy of adsorption measurement. Methane adsorption isotherms on Marcellus shale are measured at 303, 313, 323 and 333 K with pressure up to 3000 psi. A fugacity-based Dubinin-Astakhov (D-A) isotherm is implemented to correct for the non-ideality and predict the temperature-dependence of supercritical gas sorption. The Marcellus shale studied displays generally linear correlations between adsorption capacity and pressure over the range of temperature and pressure investigated, indicating the presence of a solute gas component. It is noted that the condensed phase gas storage exists as the adsorbed gas on shale surface and dissolved gas in kerogen, where the solute gas amount is proportional to the partial pressure of that gas above the solution. To our best understanding, it is the first time to observe the contribution of dissolved gas to total gas storage. With adsorption potential being modeled by a temperature dependence expression, the D-A isotherm can successfully describe supercritical gas sorption for shale at multiple temperatures. Adsorption capacity remarkably decreases with temperature attributed to the isosteric heat of adsorption. Lastly, the wide applicability of the proposed fugacity-based D-A model is also tested for literature adsorption data on Woodford, Barnett, and Devonian shale. Overall, the fugacity-based D-A isotherm provides precise representations of the temperature-dependent gas adsorption on shales investigated in this work. The application of the proposed adsorption model allows predicting adsorption data at multiple temperatures based on the adsorption data collected at a single temperature. This study lays the foundation for accurate evaluation of gas storage in shale.

A significant amount of research for gridding of complex reservoirs, including models with fractures, has focused on use of unstructured grids. While models with unstructured grids can be extremely flexible, they can also be expensive, both in configuring, computationally, and visual display. Even with this focus on unstructured grids, most reservoir simulation models are still built on structured grids. Current methods for creating reservoir simulation models with structured grids often involve defining a base grid upfront and then "somehow" inserting one or more Features of Interest (FOI's) into the model. Applied to fractured horizontal wells with many stages it can be extremely difficult to accurately align wells and completions within a pre-existing simulation grid. This work describes and demonstrates a methodology to resolve such issues. This approach changes the order of model design and creation steps. This paper describes the process where FOI's are identified, a base grid is designed around the FOI's, then local grid refinements (LGR's) are defined as desired. Applied to a horizontal well with fractures, the well and completion locations are defined before the detailed grid definition is created. This process is illustrated for generalized FOI's, and then applied to fractured horizontal wells. Formulas for creation of models for wells with evenly space homogeneous completions are presented. Numerical testing and analyses are presented that show the impact of the gridding parameters and various design parameters on performance of reservoir simulations.

In this study, an extended version of the fractional decline model is analytically developed for gas flow in fracture reservoir using the anomalous diffusion equation incorporated with the fractional calculus and equation of state. The model can represent the heterogeneity of complex fracture networks and can further be used to interpret reservoir properties by performing type-curve matching of flow rate and cumulative production from multi-fractured horizontal wells in unconventional reservoirs. To address the limitations of conventional planar fracture idealization, the hydraulic fractures in this present study are integrated with the fracture network, and the fractional diffusivity is solved for a horizontal wellbore. Upon establishing and solving the governing equation in the Laplace domain, the solutions are converted back to the real-time and space domain by performing numerical Laplace inversion. A set of distinctive type curves is generated on the basis of an infinite conductivity horizontal well model, considering early and middle times, in order to capture the heterogeneity of the fractal network in the reservoir model. Application of this new model is demonstrated through type-curve matching of two synthetic cases of simulation data obtained from commercial software; the cases cover orthogonal evenly and unevenly distributed networks. Results from these examples show an acceptable match between the fractional decline model and synthetic data and, hence, showcase the applicability of this model to capture the transient flow in heterogeneous fractured reservoirs.

Cement quality is typically determined through the use of sonic logging tools, more commonly known as cement bond logs (CBLs), or more recently ultrasonic imaging tools (USITs). In general, these tools have served the industry well over time, but with the advent of new and exotic cement blends, as well as multistage cement jobs in today's unconventional horizontal wells, the quality and even location of the cement has become more problematic for basic CBL/USIT tools to detect. In addition, these tools are ineffective through multiple uncemented casing strings. A novel method to detect cement was developed as an offshoot of a technology used for detecting proppant in hydraulically fractured wells. This technique uses a non-radioactive tracer which exhibits a high thermal neutron capture cross section that is then incorporated into the proppant grains during manufacture. The proppant can then be detected using standard neutron-logging tools, at any time during the well's life. By incorporating small volumes of this detectible proppant into the cement slurry, the cement can then be detected using the same logging tools. This leads to identification of the top of cement, as well as the cement quality. If desired, the taggant can be staged such that the top and bottom of a cement stage can be detected. This paper will first review the industry concerns with cement detection. It will then discuss the principles and theory behind how the taggant works, both for basic proppant detection, as well as the novel application as a vehicle for cement detection. This will also include lab testing showing no impact of the tagged proppant on cement performance. The authors will conclude by presenting several case histories of cement detection, including two horizontal well applications, one each in the Permian and Canada. A third case history will also be presented in which the cement was detected through multiple strings of uncemented casing, to verify success of a cement squeeze in a surface casing remediation. This new technique allows for cement detection in wells in which conventional CBL/USITs are difficult to interpret, including detection of exotic cement blends, and through multiple strings of casing. This allows for more confidence in cement isolation, particularly in today's unconventional wells, where isolation of uphole formations is critical. This paper will be useful for drilling and completion engineers who are concerned with their ability to confirm cement quality, as well as production engineers who must perform remedial cementing operations.

This study presents a new type-curve method to characterize hydraulic fracture (HF) attributes and dynamics by analyzing two-phase flowback data from multi-fractured horizontal wells (MFHWs) in hydrocarbon reservoirs.The proposed method includes a semianalytical model, as well as a workflow to estimate HF properties (i.e., initial fracture pore-volume and fracture permeability) and HF closure dynamics (through iterating fracture compressibility and permeability modulus).The semianalytical model considers the coupled two-phase flow in the fracture and matrix system, the variable production rate at the well, as well as the pressure-dependent reservoir and fluid properties. By incorporating the contribution of fluid influx from matrix into the fracture effective compressibility, a new set of dimensionless groups is defined to obtain a dimensionless solution for type-curve analysis. The accuracy of the proposed method is tested using the synthetic data generated from six numerical simulation cases for shale gas and oil reservoirs. The numerical validation confirms the unique behavior of type curves during fracture boundary dominated flow and verifies the accuracy of the type-curve analysis in the characterization of fracture properties. For field application, the proposed method is applied to two MFHWs in Marcellus shale gas and Eagle Ford shale oil.The agreement of interpreted results between the proposed method and straight-line analysis not only demonstrates the practicality in field application but also illustrates the superiority of the type-curve method as an easy-to-use technique to analyze two-phase flowback data. The analysis results from both of the field examples reveal the consistency in the estimated fracture properties between the proposed method and long-term history matching.

Surfactant performance is a function of its hydrophobic tail, and hydrophilic head in combination with crude oil composition, brine salinity, rock composition, and reservoir temperature. Specifically, for nonionic surfactants, temperature is a dominant variable due to the nature of the ethylene oxide (EO) groups in the hydrophilic head known as the cloud point temperature. This study aims to highlight the existence of temperature operating window for nonionic surfactants to optimize oil recovery during EOR applications in unconventional reservoirs. Two nonylphenol (NP) ethoxylated nonionic surfactants with different EO head groups were investigated in this study. A medium and light grade crude oil were utilized for this study. Core plugs from a carbonate-rich outcrop and a quartz-rich outcrop were used for imbibition experiments. Interfacial tension and contact angle measurements were performed to investigate the effect of temperature on the surfactant interaction in an oil/brine and oil/brine/rock system respectively. Finally, a series of spontaneous imbibition experiments was performed on three temperatures selected based on the cloud point of each surfactant in order to construct a temperature operating window for each surfactant. Both nonionic surfactants were observed to improve oil recovery from the two oil-wet oil/rock system tested in this study. The improvement was observed on both final recovery and rate of spontaneous imbibition. However, it was observed that each nonionic surfactant has its optimum temperature operating window relative to the cloud point of that surfactant. For both nonionic surfactants tested in this study, this window begins from the cloud point of the surfactant up to 25°F above the cloud point. Below this operating window, the surfactant showed subpar performance in increasing oil recovery. This behavior is caused by the thermodynamic equilibrium of the surfactant at this temperature which drives the molecule to be more soluble in the aqueous-phase as opposed to partitioning at the interface. Above the operating window, surfactant performance was also inferior. Although for this condition, the behavior is caused by the preference of the surfactant molecule to be in the oleic-phase rather than the aqueous-phase. One important conclusion is the surfactant achieved its optimum performance when it positions itself on the oil/water interface, and this configuration is achieved when the temperature of the system is in the operating window mentioned above. Additionally, it was also observed that the 25°F operating window varies based on the characteristic of the crude oil. A surfactant study is generally performed on a single basin, with a single crude oil on a single reservoir temperature or even on a proxy model at room temperature. This study aims to highlight the importance of applying the correct reservoir temperature when investigating nonionic surfactant behavior. Furthermore, this study aims to introduce a temperature operating window concept for nonionic surfactants. This work demonstrates that there is not a "one size fits all" surfactant design.

In 2011, the Society of Petroleum Evaluation Engineers (SPEE) published Monograph 3 as an industry guideline for reserves evaluation of unconventionals, especially for probabilistic approaches. This paper illustrates the workflow recommended by Monograph 3. The authors also point out some dilemmas one may encounter when applying the guidelines. Finally, the authors suggest remedies to mitigate limitations and improve the utility of the approach. This case study includes about 300 producing shale wells in the Permian Basin. Referring to Monograph 3, analogous wells were identified based on location, geology, drilling-and-completion (D&C) technology; Technically Recoverable Resources (TRRs) of these analogous wells were then evaluated by Decline Curve Analysis (DCA). Next, five type-wells were developed with different statistical characteristics. Lastly, a number of drilling opportunities were identified and, consequently, a Monte Carlo simulation was conducted to develop a statistical distribution for undeveloped locations in each type-well area. The authors demonstrated the use of probit plots and demonstrated the binning strategy, which could best represent the study area. The authors tuned the binning strategy based on multiple yardsticks, including median values of normalized TRRs per lateral length, slopes of the distribution lines in lognormal plots, ratios of P10 over P90, and well counts in each type-well category in addition to other variables. The binning trials were based on different geographic areas, producing reservoirs, and operators, and included the relatively new concept of a "learning curve" introduced by the Society of Petroleum Engineers (SPE) 2018 Petroleum Resources Management System (PRMS). To the best of the authors’ knowledge, this paper represents the first published case study to factor in the "learning curves" method. This paper automated the illustrated workflow through coded database queries or manipulation, which resulted in high efficiencies for multiple trials on binning strategy. The demonstrated case study illustrates valid decision-making processes based on data analytics. The case study further identifies methods to eliminate bias, and present independent objective reserves evaluations. Most of the challenges and situations herein are not fully addressed in Monograph 3 and are not documented in the regulations of the U.S. Security and Exchange Commission (SEC) or in the PRMS guidelines. While there may be differing approaches, and some analysts may prefer alternate methods, the authors believe that the items presented herein will benefit many who are starting to incorporate Monograph 3 in their work process. The authors hope that this paper will encourage additional discussion in our industry.