Barnett Shale Formation Research Articles

Optimized schemes of enhanced shale gas recovery by CO2-N2 mixtures associated with CO2 sequestration

In response to the growing demand for CO2 mitigation and unconventional natural gas, the oil and gas industry is researching for the viability to enhance shale gas recovery (ESGR) and sequester CO2 in depleted reservoirs. Previous research has shown that a CO2-N2 mixture can be employed as a desired injection fluid to concurrently address both industrial issues. Due to the infancy of ESGR operations, there is no thorough knowledge of field-level optimized strategies using CO2-N2 for the ESGR associated with CO2 sequestration. In this study, the commercial reservoir simulator GEM and CMOST by CMG were adopted to conduct ∼ 120 simulation scenarios based on the Barnett shale formation in order to comprehend the impacts of three dominating operational parameters and propose the corresponding solutions. This study reveals four major findings. Firstly, a larger N2 concentration corresponds to a better recovery factor of shale gas but a shorter breakthrough time that causes the worse CO2 storage performance. Secondly, an injection rate is initially proportional to the shale gas recovery and CO2 storage performance but beyond a threshold rate, it can inversely affect both outcomes. Thirdly, the influence of a soaking period is limited in a short term and negligible in a long term for both ESGR and CO2 sequestration. Lastly, by selecting an appropriate combination of an injection rate and a CO2-N2 ratio, the income of a project can significantly be improved by 22 % under the current carbon tax policy. This study provides shale gas operators with a guideline for implementing large-scale ESGR projects employing CO2-N2 mixtures.

Surrogate-assisted hydraulic fracture optimization workflow with applications for shale gas reservoir development: a comparative study of machine learning models

Unconventional reservoirs have become the main alternative for increasing oil and gas reserves around the world. Owing to their ultralow permeability properties and special pore structure, hydraulic fracturing technology is necessary to realize the efficient development and economic management of unconventional resources. To maximize the production capacity of wells, several fracture parameters, including fracture number, length, width, conductivity, and spacing, need to be optimized effectively. The optimization of hydraulic fracture parameters in shale gas reservoirs generally demands intensive computations owing to the necessity of numerous physicalmodel simulations. This study proposes a machine learning (ML)–assisted global optimization framework to rapidly obtain optimal fracture parameters. We employed three supervised ML models, including the radialbasis function, K-nearest neighbor, and multilayer perceptron, to emulate the relationship between fracture parameters and shale gas productivity for multistage fractured horizontal wells. Firstly, several forward shale gas simulations with embedded discrete fracture models generate training samples. Then, the samples are divided into training and testing samples to train these ML models and optimize network hyper parameters, respectively. Finally, the trained ML models are combined with an intelligent differential evolution algorithm to optimize the fracture parameters. This novel method has been applied to a naturally fractured reservoir model based on the real-field Barnett shale formation. The obtained results are compared with those of conventional optimizations with high-fidelity models. The results confirm the superiority of the proposed method owing to its very low computational cost. The use of ML modeling technology and an intelligent optimization algorithm could greatly contribute to simulation optimization and design, prompting progress in the intelligent development of unconventional oil and gas reservoirs in China.

Multiproxy geochemical characterization of Kommugudem Formation, Krishna Godavari Basin, India: Implication on hydrocarbon potential and shale brittleness

The unconventional reserves are vital future energy resources and globally augmented interest endeavours the discovery of gas and oil‐rich shales. The Kommugudem Formation, Krishna Godavari Basin was evaluated based on sample laboratory and well log studies from six wells deciphering mineralogical and organic geochemical parameters to provide an understanding of hydrocarbon potential and brittleness. Kommugudem Formation constitutes Type III kerogen dominantly with high total organic carbon (TOC) and Tmax indicating the presence of high organic matter. The mean vitrinite reflectance indicates mature to post‐mature shale majorly in the gas generation window. Fourier transform infrared spectroscopy functional group investigation revealed the presence of aromatics and aliphatics with a higher degree of abundance owing to higher TOC content. The basic well logs recorded in the studied region are used to estimate the continuous TOC and porosity of the formation and corroborate with laboratory core data. Petrographic, X‐ray diffraction, and field emission scanning electron microscopy studies revealed the presence of dominantly siliceous mineral matter with relatively lower clay and carbonates. Organic mudstone classification indicates formation is dominantly silica‐dominated lithotype and clay‐rich siliceous mudstone. Microfractures and micropores are observed in mineral and organic matter that may act as storage sites for hydrocarbons. Kommugudem Formation on the basis of mineralogy indicates compositional analogy with brittle zones of Barnett Shale Formation. The high mineralogical brittleness index of the Kommugudem Formation indicates good conditions for hydraulic fracturing. The present investigation demonstrates excellent hydrocarbon generation potential and good brittleness in wells A and B while C, D, E, and F have relatively lower potential and may have low gas and oil sources.

Evaluation of CO2 Injection in Shale Gas Reservoirs Based on Numerical Simulation

Shale gas is natural gas that is found trapped within a typical sedimentary rock derived from clastic sources which is known as shale. The combination of two advanced technologies (horizontal drilling and hydraulic fracturing) has helped to access to a large volumes of shale gas that was previously not producible. In this paper, the performance of CO2 injection and CH4 recovery in shale gas reservoirs is evaluated. The study reveals that it is a complex function a several parameters such as CO2 injection time, soaking time, injection volume, reservoir permeability, reservoir porosity, and thickness. Numerical simulation is performed to model CO2 huff-n-puff process and multicomponent Langmuir isotherms in the Barnett Shale formation; two horizontal wells are performed for this case. Results show that CO2-EGR is very efficient for shale gas and a recovery factor of 82.6% can be obtained by this process. Besides, an analytical study is done in order to facilitate the estimation of recovery factor based on critical parameters obtained from simulation. A comparison study of the observed data from simulation and the estimated date from some production decline models such as LGA (Logistic Growth Analysis) model, and Ali model is also performed in this study

Improved well logs clustering algorithm for shale gas identification and formation evaluation

The identification of lithology, fluid types, and total organic carbon content are of great priority in the exploration of unconventional hydrocarbons. As a new alternative, a further developed K-means type clustering method is suggested for the evaluation of shale gas formations. The traditional approach of cluster analysis is mainly based on the use of the Euclidean distance for grouping the objects of multivariate observations into different clusters. The high sensitivity of the L2 norm applied to non-Gaussian distributed measurement noises is well-known, which can be reduced by selecting a more suitable norm as distance metrics. To suppress the harmful effect of non-systematic errors and outlying data, the Most Frequent Value method as a robust statistical estimator is combined with the K-means clustering algorithm. The Cauchy-Steiner weights calculated by the Most Frequent Value procedure is applied to measure the weighted distance between the objects, which improves the performance of cluster analysis compared to the Euclidean norm. At the same time, the centroids are also calculated as a weighted average (using the Most Frequent Value method), instead of applying arithmetic mean. The suggested statistical method is tested using synthetic datasets as well as observed wireline logs, mud-logging data and core samples collected from the Barnett Shale Formation, USA. The synthetic experiment using extremely noisy well logs demonstrates that the newly developed robust clustering procedure is able to separate the geological-lithological units in hydrocarbon formations and provide additional information to standard well log analysis. It is also shown that the Cauchy-Steiner weighted cluster analysis is affected less by outliers, which allows a more efficient processing of poor-quality wireline logs and an improved evaluation of shale gas reservoirs.

Effective machine learning identification of TOC-rich zones in the Eagle Ford Shale

Successful hydrocarbon production in the Eagle Ford relies on technological advances such as directional geosteering, horizontal drilling, and hydraulic fracturing, as well as the identification and delineation of organic-rich intervals and fracture zones. Total organic carbon (TOC) is an excellent indicator for the organic richness and the hydrocarbon potential of shale in unconventional reservoirs; however, common methods for TOC estimation have many underlying assumptions and rely on empirical formulas. It is useful to develop a robust data-driven approach that could be used to reliably identify TOC-rich zones in unconventional plays such as the Eagle Ford. Using gamma-ray, deep resistivity, and sonic wireline well log data from La Salle County, South Texas, we generate a layer unit database and label the layer units using core measured TOC values. We apply a data-driven binary support vector machine (SVM) machine learning approach to identify TOC-rich zones within the Eagle Ford. We evaluate the performance of the SVM classifier and obtain F1 scores ranging from 97.4% to 99.4%. The methodology successfully identifies TOC-rich zones that match with geological observations, the ΔlogR method, and independently obtained core TOC measurements. We demonstrate the successful cross-well application of the methodology within the Eagle Ford Play Area and also demonstrate the successful cross-basin application of the methodology using data from the Barnett Shale Formation, Fort Worth Basin, North Texas, and the Duvernay Shale Formation of the West Canadian Sedimentary Basin, Canada.

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index (BI) from basic suites of well logs is desirable as it provides a useful indicator of the fracability of tight formations. Measuring mineralogical components in rocks is expensive and time consuming. However, the basic well log curves are not well correlated with BI so correlation-based, machine-learning methods are not able to derive highly accurate BI predictions using such data. A correlation-free, optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation (Texas). This transparent open box (TOB) algorithm matches data records by calculating the sum of squared errors between their variables and selecting the best matches as those with the minimum squared errors. It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error (RMSE) between calculated and predicted (BI). The prediction accuracy achieved by TOB using just five well logs (Gr, ρb, Ns, Rs, Dt) to predict BI is dependent on the density of data records sampled. At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE ~ 0.056 and R2 ~ 0.790. At a sampling density of about one sample per 0.1 ft BI is predicted with RMSE ~ 0.008 and R2 ~ 0.995. Adding a stratigraphic height index as an additional (sixth) input variable method improves BI prediction accuracy to RMSE ~ 0.003 and R2 ~ 0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of > ± 0.1. The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measurements but with similar lithofacies and burial histories. The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.

Total Organic Carbon Predictions from Lower Barnett Shale Well-log Data Applying an Optimized Data Matching Algorithm at Various Sampling Densities

Accurately estimating total organic carbon (TOC) from suites of well logs is essential as it is too costly and time consuming to take direct measurements from core samples in many wells. Unfortunately, the several methods developed over recent decades, based on various correlations and correlation-based machine learning methods, do not provide universally reliable, accurate or easily auditable TOC predictions. A method is developed and its viability evaluated exploiting a promising correlation-free, data-matching routine. This is applied to published well-log curves, with supporting mineralogical data and measured TOC, for two wells penetrating the Lower Barnett Shale formation at distinct settings within the Fort Worth Basin (Texas, U.S.). The method combines between 5 and 10 well log features and evaluates, on a supervised learning basis, multiple cases for nine distinct models at data- record-sampling densities ranging from one record for every 0.5 ft to one record for every 0.04 ft. At zoomed-in sampling densities the model achieves TOC prediction accuracies for the models combining data from both wells of (RMSE ≤ 0.3% and R2 ≥ 0.955) for models involving 6 and 10 input variables. It is the models involving six input variables that have the potential to be applied in unsupervised circumstances to predict TOC in surrounding wells lacking measured TOC, but that potential requires confirmation in future multi-well studies.

Development of a full 3D numerical model to investigate the hydraulic fracture propagation under the impact of orthogonal natural fractures

Although hydraulic fracturing has been massively studied and applied as a key technique to enhance the gas production from tight formations, some problems and uncertainties exist to accurately predict and analyze the fracture behavior in complex reservoirs, especially in the naturally fractured reservoirs like shale reservoirs. This paper presents a full 3D numerical model (FLAC3D) to study hydraulic fracturing behavior under the impact of preexisting orthogonal natural fractures. In this numerical model, the hydraulic fracture propagation direction is assumed perpendicular to the minimum principal stress and activated only by tensile failure, whereas the preexisting natural fractures can be activated by tensile or shear failure or a combination of them, and only tensile failure can open the natural fracture as well. The newly developed model was used to study the impact of preexisting orthogonal natural fractures on hydraulic fracturing behavior, based on a multistage hydraulic fracturing operation in a naturally fractured reservoir from the Barnett Shale formation, northwest of Texas in USA. In this multistage operation, two more representative stages, i.e., stage 1 with a relatively large horizontal stress anisotropy of 3.3 MPa and stage 4 with a comparatively small one of 1.3 MPa, were selected to conduct the simulation. Based on the numerical results, one can observe that the interaction between hydraulic and natural fracture is driven mainly by induced stress around fracture tip. Besides, the horizontal stress anisotropy plays a key role in opening the natural fracture. Thus, no significant opened fracture is activated on natural fracture in stage 1, while in stage 4 an opened fracture invades to about 90 m into the first natural fracture. Conversely, the hydraulic fracture length in stage 1 is much longer than in stage 4, as some fluid volume is stored in the opened natural fracture in stage 4. In this work, the shear failure on natural fractures is treated as the main factor for inducing the seismic events. And the simulated seismic events, i.e., shear failure on natural fractures, are very comparable with the measured seismic events.

Electrodialysis of concentrated brines: Effects of multivalent cations

Electrodialysis is an electrochemically driven membrane technology usually used for treating feed water containing several thousand mg/l total dissolved solids (TDS). This research investigated the use of conventional ED for the treatment of flowback water from shale gas hydraulic fracturing that contains up to tens of thousands of mg/l TDS and contain a mixture of multivalent cations. This paper discusses efforts to treat concentrated brines with electrodialysis and focuses on the mitigation of the detrimental effects of multivalent cations such as calcium, magnesium, barium, iron, and in the feedstock. A previous paper detailed the effects of calcium under defined lab conditions. The apparent calcium fouling (in and around) the electrode cells was mitigated by changing the single cathode chamber boundary membrane to a (CMX-S) that was more selective for monovalent cations. This paper covers efforts undertaken to mitigate problems associated with high concentrations of calcium, barium, iron, and magnesium. Four field samples of produced water from oil and gas operations in from the Marcellus shale formation (generally Pennsylvania, USA) and the Barnett shale formation (generally north central Texas, USA) were also tested. These results from field samples were similar to the results from lab defined tests. Mechanisms of process interference by the different multivalent cations appear different for each species. Calcium, magnesium, barium, and iron were preferentially transported from the diluate to the concentrate compared to sodium by a factor of about 1.4:1 on a charge basis resulting in a suppression of the flux of sodium to the concentrate. A single CMX-S membrane selective for monovalent cations was placed at the cathode barrier resulting in a reduced flux of calcium, magnesium and barium into the electrode rinse. However total ion flux was inhibited caused by apparent blockage of the cation transfer sites on the CMX-S membrane by the stagnant multivalent cations. A series of tests with periodic pulse polarity-reversal (anode and cathode reversed for a brief time) indicted the blockage by calcium and barium was immediately reversable, however, iron was more problematic and may have precipitated on membrane surfaces other than the cathode barrier.

Atmospheric inverse modeling via sparse reconstruction

Abstract. Many applications in atmospheric science involve ill-posed inverse problems. A crucial component of many inverse problems is the proper formulation of a priori knowledge about the unknown parameters. In most cases, this knowledge is expressed as a Gaussian prior. This formulation often performs well at capturing smoothed, large-scale processes but is often ill equipped to capture localized structures like large point sources or localized hot spots. Over the last decade, scientists from a diverse array of applied mathematics and engineering fields have developed sparse reconstruction techniques to identify localized structures. In this study, we present a new regularization approach for ill-posed inverse problems in atmospheric science. It is based on Tikhonov regularization with sparsity constraint and allows bounds on the parameters. We enforce sparsity using a dictionary representation system. We analyze its performance in an atmospheric inverse modeling scenario by estimating anthropogenic US methane (CH4) emissions from simulated atmospheric measurements. Different measures indicate that our sparse reconstruction approach is better able to capture large point sources or localized hot spots than other methods commonly used in atmospheric inversions. It captures the overall signal equally well but adds details on the grid scale. This feature can be of value for any inverse problem with point or spatially discrete sources. We show an example for source estimation of synthetic methane emissions from the Barnett shale formation.

Coupled fluid flow-geomechanics simulation in stress-sensitive coal and shale reservoirs: Impact of desorption-induced stresses, shear failure, and fines migration

Long-term pore pressure depletion significantly alters reservoir stresses, which are known to have a substantial impact on permeability in fractured reservoirs. Increased effective stresses resulting from depletion often induce a decrease in permeability. The opposite has been observed in some reservoirs with an organic rock matrix that exhibits strong sorption-mechanical coupling. With depletion, adsorbed gas desorbs from micropores resulting in shrinkage of the rock matrix, relaxation of effective stresses, and opening of fractures. In addition, reservoir depletion results in an increased stress anisotropy, which may lead to potential reactivation of critically oriented natural fractures and shear failure. The objective of this study is to develop a reservoir simulator with a full poromechanical coupling accounting for sorption-induced change of stresses, shear failure, fines production, and their effect on permeability. This paper aims to estimate the influence of the various mechanical and transport parameters affecting reservoir permeability and to predict its evolution during reservoir depletion. We compare two natural gas reservoirs with strong (San Juan coal basin) and weak (Barnett shale formation) sorption-mechanical coupling. The results of the study highlight the interplay between mechanical moduli, swelling isotherm parameters, fracture compressibility, and rock strength in determining their impact on fracture permeability evolution during depletion. We show that simple stress-dependent permeability models cannot capture permeability evolution in the presence of shear failure and fines production. A modified permeability equation is introduced to describe fines migration and shear dilation. Numerical simulation confirmed that desorption-induced strains in shales may induce changes of horizontal stresses of several MPa. These changes of stress may have a minor effect on permeability but can significantly affect horizontal stress anisotropy and should be considered while planning refracturing.

Total organic carbon estimation in shale-gas reservoirs using seismic genetic inversion with an example from the Barnett Shale

Our goal is to invert total organic carbon (TOC) using the multilayer perceptron neural network. Three-dimensional seismic data recorded near two horizontal wells drilled in the lower Barnett Shale Formation are used as input to train a neural machine, while the calculated acoustic impedance from sonic and density well-log data for the horizontal wells is used as output. The multilayer perceptron neural machine is trained in a supervised mode, and weights of connections are calculated. The full 3D seismic data are then propagated through this machine, and a cube of acoustic impedance is inverted. A crossplot of the acoustic impedance versus the TOC is used to provide a linear relationship between these two parameters. This relationship is used to suggest a 3D TOC cube from the inverted cube of the acoustic impedance. Obtained results are checked with the Schmoker's TOC of another horizontal well drilled in the lower Barnett. These results show the ability of the genetic inversion to enhance characterization of shale-gas reservoirs.

Lithologic and geomechanical facies classification for sweet spot identification in gas shale reservoir

Unconventional reservoirs require advanced technologies such as horizontal well placement and hydraulic fracturing to be successfully exploited at economic rates. In this context, static and dynamic reservoir quality (RQ) concepts are introduced. Static RQ or standard RQ comprises a set of petrophysical parameters that describe formation tendency for development. Dynamic RQ or completion quality is defined by a set of geomechanical parameters that estimate formation tendency to be fractured. The convergence of static and dynamic RQs allows for evaluating the production potential of a field; particularly, productive sweet spots are located in those intervals in which good static and dynamic RQs are detected. We have developed a workflow to identify producible intervals in unconventional reservoirs by means of lithologic and geomechanical facies classification. Starting from core data, a clustering technique is used to create a set of lithologic facies that are then extended to the logged interval and characterized in terms of static RQ. The same approach is used to classify the logged interval with a set of geomechanical facies in which dynamic RQ is estimated. The integration of lithologic and geomechanical facies leads to sweet spot identification. Workflow application to available data from the Barnett Shale Formation allows us to classify the logged interval with four log facies (LF) and five geomechanical facies (GF) and to identify productive sweet spots in the upper and middle Lower Barnett. Eventually, LF and GF are linked to seismic facies probability volumes and Young’s modulus from elastic inversion of surface seismic. Seismic-driven geostatistical realization of LF and GF provides static and dynamic RQs volumes that are combined into volumes of productive and nonproductive facies.

Anoxia controlled by relative sea-level changes: An example from the Mississippian Barnett Shale Formation

A continuous core drilled in the Barnett Shale Formation (Fort Worth Basin, Texas, United States), a remarkable example of Paleozoic organic-rich sediments deposited in a narrow inland seaway, was analysed by means of sedimentological, geochemical and biostratigraphic data. As the influence of sea-level fluctuations on benthic anoxia in ancient seas is still poorly understood, our work aims to shed light on the control exerted by relative sea-level variations on the paleoceanographic processes that drove fine-grained sediment deposition, organic matter dispersal and preservation during the sedimentation of the Barnett Shale Formation. Using spectral gamma-ray log a large second-order sequence modulated by a total of thirteen third-order sequences was inferred. Third-order relative sea-level cycles modulated the level of restriction of the basin through time, alternating pulses of bottom-water oxygenation and relatively more efficient watermass circulation with times of sluggish bottom-water and stagnation, which controlled the facies stacking pattern and organic matter preservation. In particular, relatively increased stagnation occurred during lowstand phases, while relatively reduced restriction was principally associated with transgressive and highstand systems tracts with more efficient bottom current circulation. On a larger scale, second-order eustatic cycles played a crucial role in determining the paleoceanographic and depositional processes responsible for anoxia. During the early phase of sea-level rise, the pre-existing topography was flooded establishing the conditions for a shallow strongly restricted anoxic basin. During the second-order regression the basin experienced increased isolation with bottom-water stagnation and deep-water renewal of ~6000years. Anoxia terminated abruptly, corresponding to the eustatic sea-level rise associated with the beginning of a new second-order sequence. Therefore, the Barnett Shale Formation represents a significant example of the complex interplay between sea-level-controlled basin physiography and local paleoceanographic conditions.

Integrated microseismic and geomechanical study in the Barnett Shale Formation

We have developed a careful chronological review of multistage stimulation treatments, in which microseismic interpretation was supported by fracture simulation and finite-element geomechanical simulation. The interpretation of a zipper frac stimulation treatment conducted in the Barnett Shale Formation in 2011 progressed from microseismic interpretation, from well-site observation, to a self-consistent understanding across multiple stages and multiple wells. Through this process, we have developed a tutorial on practical interpretation of microseismic data in the context of the hydraulic fracture treatment information (pump data) and regional structural information from interpretation of 3D seismic sections. Analysis of microseismic data included summarizing groups of events in the form of [Formula: see text]-value, [Formula: see text]-value, and extracted planar geometries. We advocate the development of a chronological interpretation, the consistency of which is tested using forward modeling. The process is iterative and relies on the forward modeling of complex fracturing and finite-element geomechanical models to inform iterations of the interpretation. Although extended case studies of this type are unsuitable for well-site analysis, we recommend their use for planning stimulation treatments for future nearby wells and to inform strategies for refracturing later in the well’s life.

Volatile organic compounds at two oil and natural gas production well pads in Colorado and Texas using passive samplers

ABSTRACTA pilot study was conducted in application of the U.S. Environmental Protection Agency (EPA) Methods 325A/B variant for monitoring volatile organic compounds (VOCs) near two oil and natural gas (ONG) production well pads in the Texas Barnett Shale formation and Colorado Denver–Julesburg Basin (DJB), along with a traffic-dominated site in downtown Denver, CO. As indicated in the EPA method, VOC concentrations were measured for 14-day sampling periods using passive-diffusive tube samplers with Carbopack X sorbent at fenceline perimeter and other locations. VOCs were significantly higher at the DJB well pad versus the Barnett well pad and were likely due to higher production levels at the DJB well pad during the study. Benzene and toluene were significantly higher at the DJB well pad versus downtown Denver. Except for perchloroethylene, VOCs measured at passive sampler locations (PSs) along the perimeter of the Barnett well pad were significantly higher than PSs farther away. At the DJB well pad, most VOC concentrations, except perchloroethylene, were significantly higher prior to operational changes than after these changes were made. Though limited, the results suggest passive samplers are precise (duplicate precision usually ≤10%) and that they can be useful to assess spatial gradients and operational conditions at well pad locations over time-integrated periods.Implications: Recently enacted EPA Methods 325A/B use passive-diffusive tube samplers to measure benzene at multiple fenceline locations at petrochemical refineries. This pilot study presents initial data demonstrating the utility of Methods 325A/B for monitoring at ONG facilities. Measurements revealed elevated concentrations reflective of production levels and spatial gradients of VOCs relative to source proximity at the Barnett well pad, as well as operational changes at the DJB well pad. Though limited, these findings indicate that Methods 325A/B can be useful in application to characterize VOCs at well pad boundaries.

New Dynamic-Surface-Tension Analysis Yields Improved Residual Surfactant Measurements in Flowback and Produced Waters

Summary Surfactant is a significant component within fracturing-fluid additives that is used for enhancing oil and gas production following stimulation of unconventional reservoirs. To help ensure optimal applications of surfactants, operating and service companies have been searching for tools for tracking surfactant residuals in flowback and produced waters, which possibly can be correlated with well productivity. This paper discusses the residual surfactant concentration measured in produced water from example Barnett shale formation wells by use of a dynamic surface tension (DST) technique, which yields much more accurate measurements. DST measurement by maximum bubble-pressure technique is well-known, but it has not been used explicitly for the application of residual surfactant measurement in produced water. A comparison of DST includes evaluating the diffusion coefficient to the reaction kinetics of adsorption of a known molecule(s) in produced water. Short-time kinetics of adsorption of different molecules vary on the basis of chemical nature and molecular size. Ultraviolet–visible (UV–Vis) spectroscopy is used to confirm the results. An estimate of residual concentrations from UV–Vis appears to be coherent with DST-measurement values. Such measurements can be useful in determining the surfactant concentration during pumping and the surfactant performance during and after hydraulic-fracturing operations. Additionally, these measurements serve a vital role in tailoring the surfactant additive to specific reservoir conditions to achieve higher oil recovery. Real-time production results from these different wells are analyzed and appear dependent on the corresponding residual surfactant concentrations from produced waters. This unique technique is new to the authors’ knowledge for its use to study and analyze produced water for surfactant-additive concentration.

Airborne Ethane Observations in the Barnett Shale: Quantification of Ethane Flux and Attribution of Methane Emissions.

We present high time resolution airborne ethane (C2H6) and methane (CH4) measurements made in March and October 2013 as part of the Barnett Coordinated Campaign over the Barnett Shale formation in Texas. Ethane fluxes are quantified using a downwind flight strategy, a first demonstration of this approach for C2H6. Additionally, ethane-to-methane emissions ratios (C2H6:CH4) of point sources were observationally determined from simultaneous airborne C2H6 and CH4 measurements during a survey flight over the source region. Distinct C2H6:CH4 × 100% molar ratios of 0.0%, 1.8%, and 9.6%, indicative of microbial, low-C2H6 fossil, and high-C2H6 fossil sources, respectively, emerged in observations over the emissions source region of the Barnett Shale. Ethane-to-methane correlations were used in conjunction with C2H6 and CH4 fluxes to quantify the fraction of CH4 emissions derived from fossil and microbial sources. On the basis of two analyses, we find 71-85% of the observed methane emissions quantified in the Barnett Shale are derived from fossil sources. The average ethane flux observed from the studied region of the Barnett Shale was 6.6 ± 0.2 × 10(3) kg hr(-1) and consistent across six days in spring and fall of 2013.

A Comprehensive Analysis of Groundwater Quality in The Barnett Shale Region

The exploration of unconventional shale energy reserves and the extensive use of hydraulic fracturing during well stimulation have raised concerns about the potential effects of unconventional oil and gas extraction (UOG) on the environment. Most accounts of groundwater contamination have focused primarily on the compositional analysis of dissolved gases to address whether UOG activities have had deleterious effects on overlying aquifers. Here, we present an analysis of 550 groundwater samples collected from private and public supply water wells drawing from aquifers overlying the Barnett shale formation of Texas. We detected multiple volatile organic carbon compounds throughout the region, including various alcohols, the BTEX family of compounds, and several chlorinated compounds. These data do not necessarily identify UOG activities as the source of contamination; however, they do provide a strong impetus for further monitoring and analysis of groundwater quality in this region as many of the compounds we detected are known to be associated with UOG techniques.