Currently the most common practice for produced water management is disposal through injection into Class IID wells with minimal treatment. This option is costly and risky, especially for wells that are located far from the nearest disposal well. Under these circumstances, treating the produced water for reuse becomes an appealing option. This study investigates disposal and treatment options in detail to determine the economic feasibility of each treatment option compared to the available disposal option. We used water production and well data from Marcellus Shale Energy and Environment Laboratory (MSEEL) to build a case study for the assessment of disposal, and membrane desalination options. We collected other necessary information such as disposal well location and desalination parameters from the Environmental Protection Agency and previously published papers. We investigated the following high salinity water treatment methods: high pressure reverse osmosis (HPRO), electrodialysis (ED), and direct contact membrane desalination (DCMD). Under the assumptions of our study, we found that disposal costs less than HPRO and DCMD. ED, on the other hand, is less expensive than disposal and provides more flexibility in terms of the quality of the produced permeate. Our results also show that if the disposal well is farther than 100 miles from the MSEEL well pad, DCMD becomes more cost-efficient than disposal. As the salinity of the MSEEL produced water drops, the cost of HPRO and ED also decreases. Produced water treatment reduces truck traffic and lowers the spill risk, emissions, and road wear. However, treatment options must be economically evaluated for each well to determine their feasibility. Our findings may serve as a guideline for economic evaluation of produced water management solutions.

An innovative method for Sucker Rod Pump (SRP) failure prediction based on Improved Completely Connected Perceptron artificial neural networks (ANN) is presented. Results are compared to historical wellbore data. ANN models validity is proven to meet SRP failures prediction with a ±5% error, helping identify and prevent potential damages, reduce costs and risks, optimize production. The process was set up in phases. In the Inception phase use cases were identified. A set of 5 million digital dynacards from 56 wells was selected altogether with complete wellbore data. Then data was processed, screened, quality controlled, created workflows and methods of use. Data driven ANN models were established and tested for an automatized pump state identification. Improved Completely Connected Perceptron models were trained based on manually labelled subsets and statistical and Elliptical Fourier Transform features. Sequential Forward Selection was applied for finding the most relevant features. Will be followed by a trend analysis, and failure prediction processes. Initially data quality was verified and further used for training several models developed to filter out bad samples. Models performance was controlled and validated for quality assurance using multifold cross validation. Results were analyzed and compared by identifying their accuracy and precision. All models performed well and although the error difference between their performances was minor, the one with highest score was selected and applied. 88% of the initial dataset was identified as of a good quality. In the second step a subset of data was manually labelled for pump states. In total, 11 states were found with "normal" being the highest occurrence, followed by "fluid pound", "gas interference" and others. This dataset was used for training the neural network models. Results were evaluated, processed and quality checked. The error analysis based on multifold cross validation and f1-score proved that resulted ANN models offer robust and consistent results, in every case up to 99% accuracy for dynacards state identification. Remaining data was fed in and QA&QC-ed. The occurrence of pump states remains the same as mentioned above. Having such models, allows the application of a trend analysis for identifying the tendency of SRP performance over time accompanied by prevention and prediction of future failures in the system. Models can be easily updated as new data comes in without the need of retraining. In case of new conditions, our established QC tools allow fast and effortless retraining with high accuracy. Principles can be extended to other artificial lift systems.

Optimizing reservoir recovery relies on successfully transforming natural geological complexity into functional flow units that can be used to predict reservoir behavior. Stratigraphic connectivity and heterogeneity can be used to identify preferential flow-paths at different scales within a reservoir. Connectivity and heterogeneity are determined using well-established stratigraphic and reservoir-management methods, including amalgamation ratios, reservoir-quality indices, and Lorenz heterogeneities. Lorenz curves have a broad utility for defining permeability–porosity relationships among well-log and model datasets. Stratigraphic Modified Lorenz Plots (SMLP) are useful for defining quality-contribution rates for specific increments of depth to identify potential flow-risk elements. Different measures of reservoir quality (i.e., Reservoir Quality Indices) highlight heterogeneity distributions and identify potentially movable oil within flow units. Stratigraphic connectivity and heterogeneity concepts can be used to identify flow units, thief zones, baffles, barriers, or depleted regions that may not be obvious from traditional well-log or sand-flag analyses. Connectivity and heterogeneity workflows for porosity, permeability, saturation, and temperature properties are applied to a high-net fluvial reservoir system to illustrate how fine-scale stratigraphic heterogeneities impact flow within a heavy oil field. Although net-to-gross and related property flags are useful for identifying opportunities, subtle variations in sand quality may not be adequately captured by simple sand-flag analyses. Reservoir facies defined by heterogeneity and connectivity can guide well-path selection and improve predictions of reservoir behavior from field- to pattern-scale. Integrated connectivity and heterogeneity workflows enable opportunity identification for steamflooding and support heat-management plans by providing geologically consistent and objective measurements that can optimize or accelerate resource maturation in new and mature fields.

New technology and energy production have been fundamental staples for improving our quality of life, creating jobs and expanding a vibrant U.S. and global economy. Fossil resources like crude oil and natural gas have been essential for accomplishing this. Main uses have been to fuel the production of electricity, generate heat for our comfort and manufacturing, and supply energy for transportation. Petroleum and natural gas also produce many important precursors for a multitude of products and materials that also have transformed our world. Lesser known is that our need for these materials is growing faster than our need for fuel. An emerging demand for precursors used to produce carbon-based substances like carbon fiber, carbon nanotubes and graphene is one reason why. These materials are composed entirely of carbon. Resources containing high amounts of carbon are needed for their production. Crude oil and natural gas are well suited for that purpose and we show that a new era of uses for these important resources is evolving. Why the interest in carbon-based materials? They are very strong, very light, and have a wide range of uncommon and extraordinary physical properties. The synthesis of these materials is rapidly gaining importance as one of the most exciting and promising innovations ever developed by man. While various sources for producing carbon-based materials have been identified, petroleum or crude oil is proving to be both suitable and preferred. In this review paper, we (i) give examples of the numerous incredible new carbon-based products and materials that are advancing and growing, (ii) briefly discuss known processes used to make the needed material precursors from petroleum, (iii) show evidence that refinery yields are shifting from fuels to materials, and (iv) cite exciting and forward looking research programs now underway. Also discussed is why lower API gravity, or higher density petroleum known as heavy oil, could be a preferred source of carbon-based material precursors. The discussion that follows gives ample reason to step back and reassess the views expressed by some on the continued importance of fossil resources and their emerging new uses in today's world.

Complete zonal isolation along with wellbore integrity is paramount to oil field development which ensures environmental safety and favorable well economics. However, the complexity of wells today increases the wellbore architecture's susceptibility to unavoidable stress events. This research focuses on utilizing aluminum oxide nanofibers (ANF's) in oil well cements. ANF's are a relatively new type of composite nanomaterial that has the potential to provide significant mechanical performance. A Scanning Electron Microscope (SEM) was used to study the morphology of ANF's. Nanofibers were ultrasonicated in water and characterized quantitatively by ultraviolet–visible spectrophotometry (UV-Vis) and qualitatively by transmission electron microscope (TEM) images. The results show ANF's have high dispersibility. Cement class "H" formulations containing different percentages of ANF's by weight of cement (BWOC) were mixed and cured with various additives for 24 hours at 76.7 °C with 24.13 MPa. Uniaxial compressive strength (UCS) tests were performed which suggested the inclusion of ANF's increases the UCS when compared to cement samples without ANF's. Although, high concentrations of ANF's results in nanofiber agglomeration which weakens the mechanical properties of the cement samples. The elastic properties of the cement samples were measured under cyclic confining pressures. All cement samples experienced low inelastic deformation during confining pressure cycling. Samples containing 0.1% ANF's BWOC possessed the lowest Modulus of Elasticity (MOE) and the highest Poisson's Ratio. This increases the cement sheath's chances of resisting deformation due to casing contraction/expansion. An X-ray diffraction (XRD) analysis was conducted to determine the hydration products. All cement formulation experienced the same XRD pattern with only variations in the diffraction peaks. Cement samples containing ANF's contained increased amounts of calcium silicate hydrate (C-S-H) gel due to the nucleation effect and the formation of tobermorite.

The goal of this work is to develop synergistic surfactant blends that can perform wettability alteration for improved oil recovery (IOR) in tight liquid-rich unconventional shale reservoirs. Shales samples from Eagle Ford and Wolfcamp formation were used in this study. The performance of these surfactant systems in altering the wettability and improving the spontaneous imbibition process was evaluated. Comprehensive screening of surfactants (anionic, non-ionic, zwitterionic) was performed in this study. First, aqueous stability tests of these surfactant systems were performed at reservoir temperature. Second, contact angle experiments were conducted on polished oil-wet shale (Eagle Ford and Wolfcamp) chips to screen formulations with optimal wettability-altering capabilities. Third, bulk emulsion stability was investigated by measuring the half-lives of the emulsions. Fourth, the interfacial tensions between oil-surfactant formulations were measured via the pendant drop method using a goniometer. Finally, spontaneous imbibition experiments in tight cores such as carbonates and shale were performed to evaluate the efficacy of these formulations in recovering oil. Several anionic surfactants and their blends with anionic, non-ionic, and zwitterionic cosurfactants were shortlisted based on the aqueous stability tests. Polished shale chips from Eagle Ford and Wolfcamp formation were made oil-wet by aging them with crude oil at 85 °C for 30 days. Contact angle experiments revealed that these synergistic surfactant blends can alter the wettability from oil-wet to water-wet conditions and outperform the performance of the individual surfactant system. The spontaneous imbibition experiments demonstrate that a significant amount of oil (11.8 to 52 % OOIP) can be recovered from tight carbonate rocks by optimally selecting surfactant formulations. It was shown that surfactant imbibition in an originally oil-wet shale sample can recover a significant amount of oil (12.96 to 22.4% OOIP). Unconventional liquid-rich shale plays such as Eagle Ford, Bakken and Wolfcamp constitute a major portion of the oil production in the US. Surfactants can significantly improve production from such shales and improve oil recovery. This study offers a novel systematic investigation of synergistic surfactant blends which alters the wettability of oil-wet shale to water-wet and induce spontaneous imbibition in liquid-rich shales to increase oil recovery.

The need for petroleum engineers to provide energy for the world population in the upcoming decades and the role of SPE in this regard are discussed in this paper. There have been recent papers suggesting that there is a diminished or no need for petroleum engineering (e.g., SPE 194764, The End of Petroleum Engineering as We Know It; SPE 195908, Petroleum Engineering Enrollment: Past, Present and Future). This is simply inaccurate and the proliferation of their message is alarming in that it may deprive the industry from having the required talent to produce the energy the world needs from natural gas and oil for decades to come. Engineering disciplines, including petroleum, have been transforming all the time. The practices we use today have little resemblance to what we did 20 years ago. They will continue to evolve, not stop, due to three major points: Although the percentage of the energy from oil and natural gas the world needs is expected to decline from about 53% to about 48% over the next 30 years, the amount of production is actually expected to increase by 20% for oil and 50% for natural gas. This is due mainly to expected increase in world population (from 7.5 to 9 billion) and improvements in the economic conditions of developing countries. Considerable advances and efforts in all aspects of petroleum engineering (from drilling under more challenging conditions to completion and production from deeper wells to increasing recovery from heavy oil and through EOR) are required to meet those higher production levels. We know that even maintaining production at the current level is a challenge. Climate change is real. As engineers we do not debate the science, and we should transform our industry in every possible way to minimize any adverse effects of our work on the environment and to comply with state regulations. One manifestation of this has been the measured increase in historical natural gas production and simultaneous decrease in CO 2 emissions. The information and manuscript of this paper were developed before the pandemic of COVID-19 affected most of the world. This is why the effects of this pandemic are not discussed. Nevertheless, it is a reasonable assumption that the pandemic will not affect the long range for energy from oil and natural gas which is the subject of this paper. Another example as one of the major factors in reducing Green House Gases is to inject and store CO 2 in underground formations. Petroleum engineers will be the ones tasked with finding answers to the challenge of injecting in already fluid-saturated formations. With the recent developments in Data Science and Engineering Analytics, there is a greater need for petroleum engineers with an understanding of physics to take advantage of these improvements and optimize the processes we use (Anadarko's SPE 187222 Creating Value by Implementing an Integrated Production Surveillance and Optimization System – An Operator's Perspective and Chevron's SPE 181437 Application of Machine Learning in Transient Surveillance in a Deep-Water Oil Field, are good examples). Required and expected efforts by various stake holders of SPE; educational institutions; national and international operators, service companies, and regulatory bodies to provide needed petroleum engineers are discussed.

In June 2018 the team embarked on an ambitious project to address the slow development pace of Alaska's 20+ billion barrels heavy oil resource via the first ever polymer flood pilot. Following the successful commencement of the pilot in August 2018, the field demonstration, supporting laboratory experiments and numerical simulation have steadily progressed. A significant amount of valuable data and lessons learned have been collected, and are reported in this paper. The ongoing pilot and the research activities is making headway toward the primary objective of validating the use of polymer flooding for extracting heavy oil in Alaska's challenging environment. The pilot is conducted in two pre-existing pairs of horizontal injectors and producers in an isolated fault block of the Schrader Bluff heavy oil reservoir at the Milne Point Field. A customized polymer blending and pumping unit injects HPAM polymer at a concentration of 1,750 ppm to achieve a target viscosity of 45 cP. Supporting coreflood laboratory experiments have focused on quantification of polymer retention in the rock, and effect of injection water salinity, polymer, and their combinations on oil recovery. The injection and production response of the pilot flood pattern is utilized to develop a history matched reservoir simulation model for forecasting oil recovery beyond the pilot. Finally, specially designed laboratory experiments address anticipated operating concerns regarding post-polymer breakthrough such as oil-water separation efficiency and polymer induced fouling of heater tubes. Polymer has been injected continuously since startup except for two short equipment modification shutdowns, and more recently a prolonged disruption due to polymer hydration issues at the J-pad field site. Cumulatively, over 600,000 lbs. of polymer has been injected, corresponding to ∼7%PV. The two producers show significant decrease in the water cut, gradually increasing oil rate, and no polymer breakthrough. Two main observations from the coreflood are a significant uncertainty in polymer retention values, and positive oil recovery response to low salinity water (2,600 mg/liter TDS). The heterogeneity in the flood pattern presents some challenges in obtaining a robust history matched simulation model. Experimental results on produced fluids treatment indicate the formation of a dense polymer deposit, at certain conditions, on heating tubes that can negatively impact the heat transfer efficiency. The scientific knowledge, including the lessons learned during unanticipated shutdowns, quality control, logistics and field data that is being acquired from this effort has referential value for other planned EOR projects. Finally, by all indications, the polymer field pilot is steadily progressing toward achieving the ultimate goal of unlocking the massive heavy oil resources on Alaska North Slope (ANS).

In recent years, the upstream oil and gas industry has witnessed significant breakthroughs in developing and deploying permanent, on-demand, and distributed temperature (DTS) and acoustic (DAS) fiber-optic monitoring systems to optimize well completions and enhance production. Beyond steady advances in hardware, challenges associated with the analysis of distributed optical data are being addressed to enable delivery of value-driven answer solutions and services. Such solutions are often nontrivial and must be driven by scientific workflows in which data-driven models, advanced analytics and most importantly, physics-based models are applied at the right scale to correlate the data to downhole events. In this study, we present a methodology for integrating state-of-the-art intelligent completion and production tools together with a robust modeling and analytics framework for the efficient development of data interpretation services for complex downhole environments. The answer product platform discussed is built on fast computational models, robust data-driven analytics, cloud-based data streaming and management services, and real-time reporting delivery systems. Several case-studies demonstrate how our fiber-optics solutions and analysis are leveraged for applications such as leak detection, fluid injection profiling, acid placement, sand detection, and water ingress. For each case-history, we discuss the operational workflow for the downhole intelligent assembly, procedures for acquiring high-resolution DTS/DAS data, and the use of advanced fast physics or data driven models used to deliver the solution. Some of the application examples (e.g. water ingress and sand detection) also demonstrate how simultaneous measurements of DTS and DAS data are often critical to detecting and devising a strategy for mitigating well-specific issues. The case-studies presented in this study demonstrate how vast amounts of data can be acquired by fiber-optic interrogation systems and subsequently interpreted in real-time, or near real-time. Such a process is enabling oil and gas operators to make data-driven decisions that drive optimized reservoir performance and proactively mitigate operational risk such as NPT due to equipment failure.

Several oil and gas industry applications of artificial intelligence have been presented in the last decade. Machine learning techniques take center stage in most presentations. Prior to performing the actual modeling in data analytic project, the first three steps in the data analytic lifecycle involve planning, data preparation and model planning. Data visualization is an important aspect in the model planning phase as it aids in selecting variables that are important for modeling and in deciding the appropriate choice of ST model to use. Hence, it is important to invest time and resource to carry out this aspect of the project in order to delineate patterns in the dataset. Researches conducted in this area in the oil and gas industry have not maximized the power of visualizations, particularly when it comes to spatio-temporal data analysis. In this paper, we present applications of spatio-temporal exploratory data analysis for oil and gas datasets using ST-plots, applied on dataset from four unconventional formations in the US: Bakken, Marcellus, Eagleford and Wolfcamp and Bone Spring formations in the Delaware Basin. Several plots are presented, including space-time plots, animations, Hovmöller diagrams,temporal histograms, and boxplots – capturing variations in oil and gas production across space and time. In the first plot, we show variation in yearly oil production across space with different panels representing time domains. This plot shows the development of an area with few wells in the initial stages and additional wells in later stages. Colors can be added to capture the magnitude of the yearly oil/gas production, normalized with days produced. Areas of the field that contribute to higher production becomes apparent. Animations can be used to present this data monthly as is typical in production data reported to state agencies. For unconventional formations, further normalization can be done with the number of completion stages or lateral length. Hovmöller diagrams are good alternatives with space on the x-axis and time on the y-axis. This plot enables us capture spatial (in)dependence of the dependent variable and can suggest the use of space as a covariate in the final model. Another plot that could be useful is the temporal histogram, in which the third dimension is time. This plot could be useful in deciphering a suitable model for the data and clearly show if the distribution or average changes over time. We highlight how these plots aid in variable selection for spatio-temporal modeling of production data. These plots are not the typical plots used in presenting oil and gas data and hence the application is new in this regard, in addition to the temporal histogram and boxplots which are new additions.

In oil and gas operations, there is a potential for NORM scale to accumulate on process piping and equipment. This scale can be removed prior to recycling or disposal of the material, yet common removal methods may be costly, time consuming, or create large volumes of secondary waste streams that must be managed. The feasibility of using a laser beam delivery to remove NORM scale was investigated because of its potential to reduce the time and cost associated with scale removal without creating large secondary waste streams. Results from this study indicate that a laser beam delivery could be an effective method to remove NORM scale from a steel surface. Background levels of alpha and beta surface contamination and gamma exposure rate were achieved for certain pipe sections when applying a laser power of at least 5 kW. Alternative laser powers, linear speeds, or incident angles may optimize the removal efficiency for thicker scales or scales with higher surface contamination or gamma exposure rates.

The objective of this study was to perform an integrated analysis to gain insight for optimizing fracturing treatment and gas recovery from Marcellus shale. The analysis involved all the available data from a Marcellus Shale horizontal well which included vertical and lateral well logs, hydraulic fracture treatment design, microseismic, production logging, and production data. A commercial fracturing software was utilized to predict the hydraulic fracture properties based on the available vertical and lateral well logs data, diagnostic fracture injection test (DFIT), fracture stimulation treatment data, and microseismic recordings during the fracturing treatment. The predicted hydraulic fracture properties were then used in a reservoir simulation model developed based on the Marcellus Shale properties to predict the production performance. In this study, the rock mechanical properties were estimated from the well log data. The minimum horizontal stress, instantaneous shut-in pressure (ISIP), process zone stress (PZS), and leak-off mechanism were determined from DFIT analysis. The stress conditions were then adjusted based on the results of microseismic interpretations. Subsequently, the results of the analyses were used in the fracturing software to predict the hydraulic fracture properties. Marcellus Shale properties and the predicted hydraulic fracture properties were used to develop a reservoir simulation model. Porosity, permeability, and the adsorption characteristics were estimated from the core plugs measurements and the well log data. The image logs were utilized to estimate the distribution of natural fractures (fissures). The relation between the formation permeability and the fracture conductivity and the net stress (geomechanical factors) were obtained from the core plugs measurements and published data. The predicted production performance was then compared against production history. The analysis of core data, image logs, and DFIT confirmed the presence of natural fractures in the reservoir. The formation properties and in-situ stress conditions were found to influence the hydraulic fracturing geometry. The hydraulic fracture properties are also impacted by stress shadowing and the net stress changes. The production logging tool results could not be directly related to the hydraulic fracture properties or natural fracture distribution. The inclusion of the stress shadowing, microseismic interpretations, and geomechanical factors provided a close agreement between the predicted production performance and the actual production performance of the well under study.

Despite the great potential of unconventional hydrocarbons, the primary recovery factor from such reservoirs remain low. The gas-injection enhanced oil recovery (EOR) has been proved to be a promising approach by both laboratory and simulation studies. However, the fluid model for characterizing gas and oil in nanoscale pores has not been well understood and developed. Erroneous results can be generated if the bulk fluids model is applied, resulting in a large uncertainty for the numerical simulations. The objective of this work is to propose an improved fluids characterization model tailored for the compositional simulation of gas huff-n-puff in unconventional reservoirs. The Peng-Robinson equation of state (PR EOS) is used as the basic thermodynamic model in this work. Both the attraction parameter and the co-volume parameter in the PR EOS are simultaneously modified for the first time to reflect the effect of molecule-wall interaction and geometric constraints. The collected experimental data are used for validating the model. The newly generated PVT data are imported into the compositional model to numerically simulate the gas huff-n-puff process in the Middle Bakken formation to investigate the influence of modified fluid property on the production and ultimate recovery. The improved fluids characterization model is validated applicable to calculate the confined properties of reservoir fluids. It is demonstrated that the phase envelope of the confined reservoir fluids tends to shrink. At reservoir temperature, the bubble-point pressure of the Middle Bakken oil is reduced by 17.32% with consideration of the confinement effect. Such a significant suppression represents a late occurrence of the gas evaporation, which implies a potentially higher production of the shale oil reservoir. Compositional simulation predicts that the enhanced oil recovery efficiency of CO 2 huff-n-puff is unsatisfactory for the specific well in this work, which is also demonstrated in the field pilot test. However, the confinement effect results in a 1.14% elevation of the oil recovery factor in 10 years production. This work not only deepens our understanding of the confinement effect on phase behavior characterization and also shed light on the computation of the thermodynamic properties of hydrocarbons in nanopores. The results also provide practical instructions for the EOR development of unconventional reservoirs.

Most waterfloods in California target sandstone formations that are unconsolidated in nature with high porosities and high permeabilities. These formations are also characterized by high Poisson ratios and low values of Young's Moduli. There has been a concern if, during the waterfloods of these types of formations, fracturing takes place at high-injection gradients. The influence of various factors on leak-off is studied in detail, indicating that with an increase in rock permeability, the leak-off velocity increases. This study included a comprehensive analysis of the characteristics of such soft formations and their responses to high injection gradients. We show that if the leak-off factors are adjusted to reflect high permeability and proper geomechanical properties, the probability of fracture formation is nil at injection gradients up to 0.9 psi/ft, for unconsolidated rooks. We computed estimated fracture width, fracture height, fracture length and noted for all three calculations, it takes gradients approaching 1psi/ft to note a non-trivial estimated value for these characteristics. This study shows that for unconsolidated formations like those in California targeted for waterfloods, the probability of fracture formation under pressure gradients of 0.9 psi/ft. is nil, and high injectivities can be exercised without the fear of fracture formation.

The formation of stable heavy oil emulsion, which may upset separation facilities and eventually lead to production impairment, is one of the most common issues encountered in the development of heavy oil reservoirs. This paper investigates the influence of various physicochemical parameters, including water cut, polymer status (sheared/unsheared), polymer concentration, demulsifier type and concentration, and the coexistence of polymer and demulsifiers on the stability of heavy oil emulsion. The viscosity of heavy oil emulsion is also studied at various water cut and polymer concentration. In this study, water-in-heavy oil emulsion was prepared at the water cut of 30% as the blank sample using heavy oil with API gravity of 14.5° and the synthetic brine. The effect of the water cut was investigated by both the bottle test method and multiple light scattering (MLS) method to validate the effectiveness and reliability of the MLS method. The other parameters were studied only through the MLS method. The results showed that the increasing water cut resulted in the decrease of heavy oil emulsion stability and could potentially invert the stable w/o emulsion to loose o/w emulsion at the phase inversion point where the emulsion viscosity peak occurred. Adding polymer, regardless of the polymer status, tended to reduce the stability of heavy oil emulsion, and the unsheared polymer contributed to less emulsion stability. However, the influence of polymer concentration was rather complicated. The emulsion stability decreased as polymer concentration increased, and further increasing polymer concentration enhanced the emulsion stability. A similar trend was also evidenced by emulsion viscosity with increasing polymer concentration. The addition of three oil-soluble emulsion breakers was able to break the heavy oil emulsion efficiently, whereas the water-soluble demulsifier had little demulsification effect. Furthermore, there existed an optimal concentration for the selected oil-soluble demulsifier to achieve the maximum separation. Although polymer itself could intensify the destabilization of heavy oil emulsion, it hindered the destabilization process of the heavy oil emulsion when the oil-soluble demulsifiers were added. This study will provide a comprehensive understanding of the factors affecting heavy oil emulsion stability.

Multistage hydraulic fracturing is the common stimulation technique for shale formations. The treatment design, formation in-situ stress, and reservoir heterogeneity govern the fracture network propagation. Different techniques have been used to evaluate the fracture geometry and the completion efficiency including Chemical Tracers, Microseismic, Fiber Optics, and Production Logs. Most of these methods are post-fracture as well as time and cost intensive processes. The current study presents the use of fall-off data during and after stage fracturing to characterize producing surface area, permeability, and fracture conductivity. Shut-in data (15-30 minutes) was collected after each stage was completed. The fall-off data was processed first to remove the noise and water hammer effects. Log-Log derivative diagnostic plots were used to define the flow regime and the data were then matched with an analytical model to calculate producing surface area, permeability, and fracture conductivity. Diagnostic plots showed a unique signature of flow regimes. A long period of a spherical flow regime with negative half-slope was observed as an indication for limited entry flow either vertically or horizontally. A positive half-slope derivative represents a linear flow regime in an infinitely conductive tensile fracture. The quarter-slope derivative was observed in a bilinear flow regime that represents a finite conductivity fracture system. An extended radial flow regime was observed with zero slope derivative which represents a highly shear fractured network around the wellbore. For a long fall-off period, formation recharge may appear with a slope between unit and 1.5 slopes derivative, especially in over-pressured dry gas reservoirs. Analyzing fall-off data after stages are completed provides a free and real-time investigation method to estimate the fracture geometry and a measure of completion efficiency. Knowing the stage properties allows the reservoir engineer to build a simulation model to forecast the well performance and improve the well spacing.

Basin-wide heterogeneity of production in unconventional resources creates additional risk in field development planning. In the past few years, several data-driven models have been developed to increase the accuracy in predicting the recovery from shale gas and tight oil wells. However, many of the machine learning methods with so called "black box" approach provide deterministic results. Therefore, understanding the uncertainty associated with different development scenarios would be difficult to obtain. We have investigated the underlying statistical distribution functions that govern the production rates and decline behavior of unconventional wells. Identification and quantification of these distribution functions provide a strong tool to accurately forecast the cumulative production of a large group of wells in an unconventional basin. By understanding the relationship among geologic characteristics of different sections of the asset, and the impact of varying drilling and completion parameters, capital allocation can be done in a more efficient manner. In this paper, we have identified the statistical distribution parameters of decline behavior is a Power Law model. In doing so, we have used unsupervised clustering techniques to find an optimal number of clusters that enable observing well behaved and identifiable underlying distribution functions. Furthermore, we quantified different types of distribution functions in a trial and error workflow to provide a tool for accurately evaluating the impact of varying geologic parameters on the decline behavior of these wells. Our results show that the leading term (or leading coefficient), which also highly correlates with long term cumulative recovery, demonstrates Gamma distribution, while the power degree (or power coefficient) demonstrate Normal distribution. Peak production rate (maximum average daily rate), terminal rate (rate after switch point), and the time of terminal rate occurrence, all demonstrate Log Normal distribution.

Horizontal well technology is one of the major improvements in reservoir stimulation. Planning and execution are the key elements to drill horizontal wells successfully, especially through depleted formations. As the reservoir has been producing for a long time, pore pressure declines, resulting in weakening hydrocarbon-bearing rocks. Drilling issues such as wellbore stability, loss circulation, differential sticking, formation damage remarkably influenced by the pore pressure decline, increasing the risk of losing part or even all the horizontal interval. This paper presents an extensive review of the potential issues and solutions associated with drilling horizontal wells in depleted reservoirs. After giving an overview of the depleted reservoir characteristics, the paper systematically addresses the major challenges that influence drilling operations in depleted reservoirs and suggests solutions to avoid uncontrolled risks. Then, the paper evaluates several real infill drilling operations through depleted reservoirs, which were drilled in different oilfields. The economic aspect associated with potential risks for drilling a horizontal well in depleted reservoirs is also discussed. The most updated research and development findings for infill drilling are summarized in the article. It is recommended to use wellbore strengthening techniques while drilling a horizontal well through highly depleted formations. This will allow using higher mud weight to control unstable shales while drilling through the production zone. Managed Pressure Drilling should be considered as the last option for highly depleted formations because it will require a greater level of investment which is not going to have a superior rate of return due to the lack of high deliverability of the reservoir. Using rotary steerable systems is favored to reduce risks related to drilling through depleted formations. Precise analysis of different drilling programs allows the drilling team to introduce new technology to reduce cost, improve drilling efficiency and maximize profit. It is the responsibility of the drilling engineer to evaluate different scenarios with all the precautions needed during the planning stage to avoid unexpected issues. The present market conditions and the advancement in technologies for drilling horizontal wells increase the feasibility of producing the depleted reservoirs economically. This paper highlights the challenges in drilling horizontal wells in highly depleted reservoirs and provides means for successfully drilling those wells to reduce risks while drilling

Activating naturally occurring nanoparticles in the reservoir (clays) to generate Pickering emulsions results in low-cost heavy oil recovery. In this study, we test the stability of emulsions generated using different types of clays and perform a parametric analysis on salinity, pH, water to oil ratio (WOR), and particle concentration; additionally, we report on a formulation of injected water used to activate the clays found in sandstones to improve oil recovery. First, oil-in-water (O/W) emulsions generated by different clay particles (bentonite and kaolinite) were prepared for both bottle tests and zeta potential measurements, then the stability of dispersion was measured under various conditions (pH and salinity). Heavy crude oils (50 to 170,000 cP) were used for all experiments. The application conditions for these clay types on emulsion generation and stability were examined. Second, sandpacks with known amounts of clays were saturated with heavy-oil samples. Aqueous solutions with various salinity and pH were injected into the oil-saturated sandpack with a pump. The recoveries were monitored while analyzing the produced samples; a systematic comparison of emulsions formed under various conditions (e.g., salinity, pH, WOR, clay type) was presented. Third, glass bead micromodels with known amounts of clays were also prepared to visualize the in-situ behavior of clay particles under various salinity conditions. The transparent mineral oil instead of opaque heavy oil was used in these micromodel tests for better visualization results. Recommendations were made for the most suitable strategies to enhance heavy oil recovery with and without the presence of clay in the porous medium; moreover, conditions and optimal formulations for said recommendations were presented. The bottle tests showed that 3% bentonite can stabilize O/W emulsions under a high WOR (9:1) condition. The addition of 0.04% of NaOH (pH=12) further improved the emulsion stability against salinity. This improvement is because of the activation of natural surfactant in the heavy oil by the added alkali—as confirmed by the minimum interfacial tension (0.17 mN/M) between the oil and 0.04% of the NaOH solution. The sandpack flood experiments showed an improved sweep efficiency caused by the swelling of bentonite when injecting low salinity fluid (e.g., DIW). The micromodel tests showed a wettability change to be more oil-wet under high salinity conditions, and the swelling of bentonite would divert incoming water flow to other unswept areas thus improving sweep efficiency. This paper presents new ideas and recommendations for further research as well as practical applications to generate stable emulsions for improved waterflooding as a cost-effective approach. It was shown that select clays in the reservoir can be activated to act as nanoparticles, but making them generate stable (Pickering) emulsions in-situ to improve heavy-oil recovery requires further consideration.

As operators shift their focus toward operating within cashflow, understanding the true potential of these unconventional resources is becoming increasingly important. Simultaneously, accurate modeling of EURs in shale wells is becoming increasingly complicated. There are multiple factors at play for this increase in complexity, key amongst them, are well interactions. Well interactions or interference have increased with the concentration of field development in core areas of various basins and have completely changed with production behavior in shale wells. The present paper handles this multi-variable problem by incorporating well design, completion and petrophysical variables in a prediction model. Furthermore, the analysis is presented from a viewpoint of parent, child, parent/child and co-completed wells to accurately understand the variability in the driving factors. Terminal decline rate in shale wells is the decline rate wells settle at once the pressure transient reaches the boundary of the well. At this point, the well transitions to a boundary dominated flow regime and continues to drain from a fixed area. Estimating the rate of terminal decline is critical in accurate EUR modeling because changes in transition point can have a significant impact on production behavior of the well and in-turn EUR. The present paper attempts to predict the transition point using an ACE Non-Linear Regression model which is trained on a large multi-variate dataset. Variables incorporated in this analysis include terminal decline month, gas-oil-ratio based of the first three months of production, horizontal length, oil EUR, proppant per foot, average distance from the base of the producing zone, nearest neighbor mean spacing, and hydrocarbon in-place. In order to determine spacing status and nearest wellbore distances, a segment-wise analytical distance approach was taken. These distances and spacing status flags were incorporated into a multi-variate model in-order to model terminal decline rates. The transformations observed from the model showed high dependence on terminal decline month and oil EUR. However, this was less pronounced in parent/child and child wells. In parent/child and child wells completion metrics and HCIP more significantly influenced production behavior. Specifically, child wells saw a higher dependence on first three-month GOR and lateral length compared to parent/child wells which had a higher dependence on proppant per foot and average distance from the base of the producing formation. Additionally, spacing showed a moderate impact on transition point and associated terminal decline rates, but overall increased spacing caused a delayed transition point and consequently a lower terminal decline rate. Understanding how cause-and-effect relationships between parent and child wells differ offers a unique perspective into production behavior and consequently provides better insights into infill wells placement and production prediction. The present paper offers a unique perspective in looking at a key decline variable, transition point, for shale reservoirs. By using multivariate analysis, it incorporates the incremental complexity of the modeling effort and attempts to provide best practices in understanding the impact on production behavior. Furthermore, by incorporating a segment-wise analytical distance approach to determine spacing, the paper adds to the existing body of literature by providing a new perspective for a well interaction standpoint and defines the cause and effect relationships within.