Miscible HC-WAG injection is a globally implemented EOR method and seems robust in so many cases. Some of the largest HC-WAG projects are found in major carbonate oil reservoirs in the Middle-East, with miscibility being the first measure to expect the success of a HC-WAG injection. Yet, several miscible injection projects reported disappointing outcomes and challenging implementation that reduces the economic attractiveness of the miscible processes. To date, there are still some arguments on the interpretation of laboratory and field data and predictive modeling. For a miscible flood, to be an efficient process for a given reservoir, several conditions must be satisfied; given that the incremental oil recovery is largely dependent on reservoir properties and fluid characteristic. Experiences gained from a miscible rich HC-WAG project in Abu Dhabi, implemented since 2006, indicate that an incremental recovery of 10% of the original oil in place can be achieved, compared to water flooding. However, experiences also show that several complexities are being faced, including but not limited to, issues of water injectivity in the mixed wettability nature of the reservoir, achieving miscibility conditions full field, maintaining VRR and corresponding flow behavior, suitability of monitoring strategy, UTC optimization efforts by gas curtailment and most importantly challenges of modeling the miscibility behavior across the reservoir. A number of mitigation plans and actions are put in place to chase the positive impacts of enhanced oil recovery by HC-WAG injection. If gas injection is controlled for gravity and dissolution along with proper understanding on the limitations of WAG, then miscible flood will lead to excellent results in the field. The low frequency of certain reservoir monitoring activities, hence less available data for assessment and modeling, can severely bound the benefits of HC-WAG and make it more difficult to justify the injection of gas, particularly in those days when domestic gas market arises. This work aims to discuss the lessons learned from the ongoing development of HC-WAG and attempts to comprehend miscible flood assessment methods.

This study investigated the ability to produce accurate multiphase flow profiles simulating the response of producing reservoirs, using Generative Deep Learning (GDL) methods. Historical production data from numerical simulators were used to train a GDL model that was then used to predict the output of new wells in unseen locations. This work describes a procedure in which data analysis techniques are used to gain insight into reservoir flow behavior at a field level based on existing historical data. The procedure includes clustering, dimensionality reduction, correlation, in addition to novel interpretation methodologies that synthesize the results from reservoir simulation output, characterizing flow conditions. The insight was then used to build and train a GDL algorithm that reproduces the multiphase reservoir behavior for unseen operational conditions with high accuracy. The trained algorithm can be used to further generate new predictions of the reservoir response under operational conditions for which we do not have previous examples in the training data set. We found that the GDL algorithm can be used as a robust multiphase flow simulator. In addition, we showed that the physics of flow can be captured and manipulated in the GDL latent space after training to reproduce different physical effects that did not exist in the original training data set. Applying the methodology to the problem of determining multiphase production rate from new producing wells in undrilled locations showed positive results. The methodology was tested successfully in predicting multiphase production under different scenarios including multiwell channelized and heterogeneous reservoirs. Comparison with other shallow supervised algorithms demonstrated improvements realized by the proposed methodology, compared to existing methods. The study developed a novel methodology to interpret both data and GDL algorithms, geared towards improving reservoir management. The method was able to predict the performance of new wells in previously undrilled locations without using a reservoir simulator.

Hydrocarbon production is commonly associated as the dispersed flow of two and more immiscible phases starting from porous media to surface facilities. In the dispersed flow, one phase is usually dispersed into another dominating phase in terms of droplets. Accurate prediction of the droplet size distribution of a dispersed phase is critical in characterizing complex flow behavior in pipe flows. In the first part of this paper, we provide the analyses of open-source experimental data on the maximum droplet size in gas-liquid annular flow and evaluate the existing theoretical models and suggest an improvement based on the experimental data analyses to predict the maximum droplet size of the entrained liquid droplets in gas-liquid annular flow. In the second part of this paper, we cover the experimental results from the open-source literature data and in-house experimental data to give the general understanding on droplet formation concepts and evaluate the existing predictive models and present a new modeling approach to determine a maximum stable droplet size of the dispersed phase in the liquid-liquid dispersed flow under turbulent flow conditions.

Recent studies have shown the utility of the Fast Marching Method and the Diffusive Time of Flight for the rapid simulation and analysis of Unconventional reservoirs, where the time scale for pressure transients are long and field developments are dominated by single well performance. We show that similar fast simulation and multi-well modeling approaches can be developed utilizing the PSS pressure as a spatial coordinate, providing an extension to both Conventional and Unconventional reservoir analysis. We reformulate the multi-dimensional multi-phase flow equations using the PSS pressure drop as a spatial coordinate. Properties are obtained by coarsening and upscaling a fine scale 3D reservoir model, and are then used to obtain fast single well simulation models. We also develop new 1D solutions to the Eikonal equation that are aligned with the PSS discretization, which better represent superposition and finite sized boundary effects than the original 3D Eikonal equation. These solutions allow the use of superposition to extend the single well results to multiple wells. The new solutions to the Eikonal equation more accurately represent multi-fracture interference for a horizontal MTFW well, the effects of strong heterogeneity, and finite reservoir extent than those obtained by the Fast Marching Method. The new methodologies are validated against a series of increasingly heterogeneous synthetic examples, with vertical and horizontal wells. We find that the results are systematically more accurate than those based upon the Diffusive Time of Flight, especially as the wells are placed closer to the reservoir boundary or as heterogeneity increases. The approach is applied to the Brugge benchmark study. We consider the history matching stage of the study and utilize the multi-well fast modeling approach to determine the rank quality of the 100+ static realizations provided in the benchmark dataset against historical data. The multi-well calculation uses superposition to obtain a direct calculation of the interaction of the rates and pressures of the wells without the need to explicitly solve flow equations within the reservoir model. The ranked realizations are then compared against full field simulation to demonstrate the significant reduction in simulation cost and the corresponding ability to explore the subsurface uncertainty more extensively. We demonstrate two completely new methods for rapid reservoir analysis, based upon the use of the PSS pressure as a spatial coordinate. The first approach demonstrates the utility of rapid single well flow simulation, with improved accuracy compared to the use of the Diffusive Time of Flight. We are also able to reformulate and solve the Eikonal equation in these coordinates, giving a rapid analytic method of transient flow analysis for both single and multi-well modeling.

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.

Trips and failures are common occurrences in the Electric Submersible Pump (ESP) systems. The random nature of these trips and failures will lead to low industry run-life and high workover costs for ESP companies and operators. To perform early detection and take corrective actions to handle the potential incidents, ESP operation data collected from downhole and surface sensors are used to perform diagnostics and prognostics to identify trips and failures. In this study, Principal Component Analysis (PCA) method serves as a pre-processing method to retain the most essential principal components to reevaluate the initial ESP system. For a single well system, the Squared Prediction Error (SPE) and Hotelling T-square statistic (T 2 ) equations are employed for numerical visualization in the new principal component space and therefore detection of the potential ESP trips or failures. For the whole well group, the score plot of three principal components provides a solution that enables to distinguish different clusters of stable operation, trip and failure regions, and diagnose the upcoming ESP trips and failures. In this way, the predictive model is bulit to continuously analyze the ESP operation and automatically perform health monitoring for any ESP system. This paper concludes that the predictive model has the potential to construct a real-time proactive surveillance system to identify dynamic anomalies and therefore predict developing trips or failures in the ESP system.

Interwell tracers are powerful reservoir surveillance tools that provide direct reservoir flow paths and dynamics, which, when integrated with near real-time production optimization, can greatly improve recovery factor, and return on investment, the so-called "Advanced Tracers System" (ATS). Applying full field ATS is attractive for resource-holders, especially for those with large waterflood operations. However, to scale up ATS to cover large fields with potentially tens to hundreds of injectors and producers, the required unique tracer variations ("barcodes") and materials and associated analysis may increase rapidly. Here, we explore different tracer injection schemes that can acquire the most information while using reduced numbers of tracers, thereby controlling costs in field operations. We tested the designs of various modified tracer injection schemes with reservoir simulations. Numerical experiments were performed on synthetic fields with multiple injector and producer wells in waterflooding patterns. Two tracer injection schemes were considered: In Scheme 1, all injectors were injected with unique tracers representing the most information-rich case. In Scheme 2, some injectors were injected with the same tracers ("recycling" the same barcodes), and some injectors received no tracer injection ("null" barcodes). Production and tracer breakthrough data was collected for history matching after waterflooding simulations on the synthetic fields. The ensemble smoother with multiple data assimilation with tracers algorithm was used for history matching. We calculated the root-mean-square errors (RMSE) between the reference data and the history matched production simulation data. To improve the statistics, 20 independent testing reference synthetic fields were constructed by randomizing the number and locations of high permeability zones crossing different injectors and producers. In all cases, the history matching algorithms largely reduced the RMSE thereby enhancing reservoir characterization. Analyzing the statistical significance with p -values among testing cases, first, as expected, the data mismatch is highly significantly lower after history matching than before history matching ( p < 0.001). Second, the data mismatch is even lower when history matching with tracers (both in Scheme 1 and 2) than without tracers ( p < 0.05), demonstrating clearly that tracers can provide extra information for the reservoir dynamics. Finally, and most importantly, history matching with tracers in Scheme 1 or in Scheme 2 result in statistically the same data mismatch ( p > 0.05), indicating the cost-saving "recycling" and "null" tracer barcodes can provide equally competent reservoir information. To the best of our knowledge, this is the first study that evaluated the history matching qualities deriving from different tracer injection schemes. We showed that through optimal designs of the tracer injections, we can acquire very similar information with reduced tracer materials and barcodes, thus reducing costs and field operational complexities. We believe this study facilitates the deployment of large-scale reservoir monitoring and optimization campaigns using tracers such as ATS.

We assess the huff-n-puff performance in ultratight reservoirs (shales) by conducting large-scale numerical simulations for a wide range of reservoir fluid types (retrograde condensate, volatile oil, black oil) and different injection gases (CO 2 , C 2 H 6 , C 3 H 8 ) by considering relative permeability hysteresis, diffusion, and sorption. A dual-porosity naturally fractured numerical compositional model is used that considers molecular diffusion and sorption to represent the flow mechanisms during the injection process. Killough's method, Langmuir's adsorption model, and Sigmund correlation are utilized to incorporate hysteresis, sorption, and diffusion, respectively. To investigate the impact of the fluid type, we consider three fluid types from Eagle Ford shale representing retrograde condensate, volatile oil, and black oil. We conduct a comprehensive evaluation of the impact of diffusion, sorption, and hysteresis on the production performance and retention of each fluid and injection gas. Eagle Ford formation is selected because it is the most actively developed shale, and it contains a wide span of PVT windows from dry gas to black oil. The simulation results show that the huff-n-puff process improves the oil recovery by 4-6% when 10% PV of gas is injected. The huff-n-puff efficiency increases with reducing gas-oil-ratio (GOR) as oil recovery from low (GOR) reservoirs is doubled, while recovery from retrograde condensate increased by 20%. C 2 H 6 provides the highest recovery for the black and volatile oil, and CO 2 provides the highest recovery for retrograde condensate fluid type. Diffusion and sorption are essential mechanisms to be considered when modeling gas injection to any fluid type in shales. However, the relative permeability hysteresis effect is not significant. Neglecting diffusion during the huff-n-puff process underestimates the oil recovery and retention capacity. The diffusion effect on the oil density reduction is observed more during the soaking period. The diffusion impact increases with higher GOR reservoirs, while the sorption impact decreases with higher GOR. The retention capacity of the injected gas decreases with higher GOR. The diffusion impact on the retention capacity increases with higher GOR. Hence sorption and diffusion must be considered when modeling the huff-n-puff process in ultratight reservoirs.

The paper describes the in-situ polymer sampling in Mangala which helped explain the performance of a large polymer flood in Mangala field in India. The Mangala field contains medium-gravity viscous crude oil. Notably, it is the largest polymer flood in India and 34% of the STOIIP has been produced in 11 years of production. Mangala was put on full field polymer flood in 2015, six years after the start of field production on water flood in 2009. Polymer flood added 93 million barrels above the anticipated water flood recovery in 6 years. Reservoir simulation models could replicate the initial Mangala polymer flood performance. However, the performance of the lower layers of Mangala (FM-3 and FM-4) continued to progressively deviate from modeling estimates. Equally importantly, the prediction of polymer breakthrough deviated significantly from modeling estimates. After 6 years and 0.7 pore volumes of polymer injection, it is apparent that field performance is equivalent to only 50-60% of the viscosity of the polymer injected at the surface. To better understand and quantify the nature and extent of polymer degradation it is necessary to gather representative down hole samples of polymer which has stayed in the reservoir conditions for a considerable length of time. Accelerated ageing studies in the lab showed HPAM can lose viscosity and precipitate after prolonged exposure to Mangala reservoir conditions with an increase in the degree of hydrolysis as the primary reason for the degradation. The concept of transfer function based on first order kinetics was used to extrapolate the laboratory results to Mangala reservoir temperatures. To test the hypothesis, a multi-disciplinary team implemented a plan to gather a representative polymer sample from the reservoir. The polymer sample had been in the reservoir for nearly 120 days and was captured in low shear and anaerobic conditions to minimize shear and oxidative degradation. The sample was tested for degree of hydrolysis by NMR method. The results confirmed that the level of hydrolysis of the injected HPAM did increase in the reservoir leading to lower viscosity and reduced lower amide concentration. Preliminary simulation studies using the concept of viscosity half-life were used to mimic the polymer degradation with time in the reservoir. The method is quite a simplistic representation of the thermal degradation, but it significantly improved the model's water cut predictions for lower layers and the full field polymer breakthrough predictions. The impact of polymer precipitation in the reservoir on the permeability is under study and it will drive the next phase of more detailed modeling.

Objectives/Scope Historically, facility outages, cyber-attacks, natural disasters, supply interruptions and other disruptions have caused significant impacts to the flow of crude oil and petroleum products. The impacts on assets, primary and secondary markets, and economic indices need to be quantified to address the effects of disruptions efficiently and effectively. Methods, Procedures, Process This paper details the design process that was used to develop an analytical tool to predict the impacts from disruptions, determine the cascading impacts on downstream markets and dependent assets and provide estimates for recovery potential. The entire petroleum supply chain, upstream, midstream, and downstream facilities, were included in the development of the modeling capability. To best capture the supply chain network and geographic dependencies, the area of interest was divided into finite regional markets based on supply and demand market dynamics and connectivity. Extensive review was conducted of historical disruptions to understand the impacts on infrastructure and the petroleum supply chain network interdependencies to support a holistic approach to disruption simulation. Algorithms were developed to determine the probability and severity of damage of disruptions at varying intensities. The modeling capability was designed to utilize a comprehensive up-to-date database and geospatial system. The database was designed considering public, private and proprietary data, the frequency of updates, validity of source, and value added. Results, Observations, Conclusions The petroleum disruption analytical modeling tool that was developed assesses disruptions to the supply chain network and predicts the duration and severity of impacts on facilities and the cascading effect on primary and secondary markets. The tool provides disruption results in tabular, graphical, and geospatial forms for individual assets, regions, and the nation as a whole. For the United States, the tool models 26 geographical regions delineated by refining sectors, mainly along the coastlines, and dependent demand markets. The crude oil and petroleum product supply and demand is balanced for each region using local supply, net imports, interregional connectivity, and the multi-modal transportation network. This paper demonstrates how the analysis tool provides business intelligence insights for the days of stocks available throughout the disruption duration and the total loss in products to the markets. An economic submodule was integrated with the tool that determines the impacts on the crude oil and gasoline price and the resulting impact on Gross Domestic Product (GDP). The model was benchmarked during Hurricane Laura 2020 and the predicted reduction in refining production was 85% of realized losses. Novel/Additive Information The innovative analytical tool simulates disruptions and provides predicted forecast of facility, market, and economic impacts for an extended period of time after the event occurs supporting response, recovery, and planning efforts. The model can be assimilated for any geographical or geopolitical region with consideration given to region specific disruptions. This paper provides case studies exemplifying use cases and model simulated results for petroleum supply disruptions.

The Permian Basin of west Texas is the largest and most prolific shale oil producing basin in the United States. Oil production from horizontal shale oil wells in the Permian Basin has grown from 5,000 BOPD in February, 2009 to 3.5 Million BOPD as of October, 2020, with 29,000 horizontal shale oil wells in production. The primary target for this horizontal shale oil development is the Wolfcamp shale. Oil production from these wells is characterized by high initial rates and steep declines. A few producers have begun testing EOR processes, specifically natural gas cyclic injection, or "Huff and Puff", with little information provided to date. Our objective is to introduce a novel EOR process that can greatly increase the production and recovery of oil from shale oil reservoirs, while reducing the cost per barrel of recovered oil. A superior shale oil EOR method is proposed that utilizes a triplex pump to inject a solvent liquid into the shale oil reservoir, and an efficient method to recover the injectant at the surface, for storage and reinjection. The process is designed and integrated during operation using compositional reservoir simulation in order to optimize oil recovery. Compositional simulation modeling of a Wolfcamp D horizontal producing oil well was conducted to obtain a history match on oil, gas, and water production. The matched model was then utilized to evaluate the shale oil EOR method under a variety of operating conditions. The modeling indicates that for this particular well, incremental oil production of 500% over primary EUR may be achieved in the first five years of EOR operation, and more than 700% over primary EUR after 10 years. The method, which is patented, has numerous advantages over cyclic gas injection, such as much greater oil recovery, much better economics/lower cost per barrel, lower risk of interwell communication, use of far less horsepower and fuel, shorter injection time, longer production time, smaller injection volumes, scalability, faster implementation, precludes the need for artificial lift, elimination of the need to buy and sell injectant during each cycle, ability to optimize each cycle by integration with compositional reservoir simulation modeling, and lower emissions. This superior shale oil EOR method has been modeled in the five major US shale oil plays, indicating large incremental oil recovery potential. The method is now being field tested to confirm reservoir simulation modeling projections. If implemented early in the life of a shale oil well, its application can slow the production decline rate, recover far more oil earlier and at lower cost, and extend the life of the well by several years, while precluding the need for artificial lift.

The aim of this study is to demonstrate the value of a fully integrated ensemble-based modeling approach for an onshore field in Abu Dhabi. Model uncertainties are included in both static and dynamic domains and valuable insights are achieved in record time of nine-weeks with very promising results. Workflows are established to honor the recommended static and dynamic modeling processes suited to the complexity of the field. Realistic sedimentological, structural and dynamic reservoir parameter uncertainties are identified and propagated to obtain realistic variability in the reservoir simulator response. These integrated workflows are used to generate an ensemble of equi-probable reservoir models. All realizations in the ensemble are then history-matched simultaneously before carrying out the production predictions using the entire ensemble. Analysis of the updates made during the history-matching process demonstrates valuable insights to the reservoir such as the presence of enhanced permeability streaks. These represent a challenge in the explicit modeling process due to the complex responses on the well log profiles. However, results analysis of the history matched ensemble shows that the location of high permeability updates generated by the history matching process is consistent with geological observations of enhanced permeability streaks in cores and the sequence stratigraphic framework. Additionally, post processing of available PLT data as a blind test show trends of fluid flow along horizontal wells are well captured, increasing confidence in the geologic consistency of the ensemble of models. This modeling approach provides an ensemble of history- matched reservoir models having an excellent match for both field and individual wells’ observed field production data. Furthermore, with the recommended modeling workflows, the generated models are geologically consistent and honor inherent correlations in the input data. Forecast of this ensemble of models enables realistic uncertainties in dynamic responses to be quantified, providing insights for informed reservoir management decisions and risk mitigation. Analysis of forecasted ensemble dynamic responses help evaluating performance of existing infill targets and delineate new infill targets while understanding the associated risks under both static and dynamic uncertainty. Repeatable workflows allow incorporation of new data in a robust manner and accelerates time from model building to decision making.

The vast shale gas and tight oil reservoirs cannot be economically developed without multi-stage hydraulic fracture treatments. Owing to the disparity in the density of natural fractures and the different in-situ stress conditions in these formations, micro-seismic fracture mapping has shown that hydraulic fracture treatments develop a range of large-scale fracture networks. The effect of these various fracture geometries on production is a subject matter in question. The fracture networks approximated with micro-seismic mapping are integrated with a commercial numerical production simulator that discretely models different network structures. Two fracture geometries have been broadly proposed, i.e., orthogonal and transverse. The orthogonal pattern represents a network with cross-cutting fractures orthogonal to each other, whereas transverse profile maps uninterrupted fractures achieving maximum depth of penetration into the reservoir. The response for a single stage is further investigated by comparing the propagation of each stage to be dendritic versus planar. A dendritic propagation is a bifurcation of the induced hydraulic fracture due to the intersection with the natural fracture (failure along the plane of weakness). For the same injected fracture treatment volume, the transverse network attains a higher penetration into the reservoir, achieves a higher stimulated reservoir volume (SRV), and produces around 40-65% more than the orthogonal network over a timespan of 10 years. The SRV will largely dictate the drainage area in a tight environment. The cumulative production rises until the pressure drawdown reaches the extent of the fracture fairway. For the orthogonal network, the unstimulated reservoir boundary is reached at a sooner time than the transverse network. It is found that by increasing the fracture spacing in both the networks from 100 ft to 150 ft, the relative production was enhanced in the orthogonal network by 41%, but when it was further increased to 200 ft- there was a minor drop (not increase) in the relative production (4.5%). For an infinite conductivity fracture, the width of the fracture has minimal effect on oil and gas production. For the dendritic pattern, the size of the SRV created due to the interaction between the induced and natural fractures largely depends on the length of natural fractures and the point of interaction (center, off-center, or extremity). Effect of length, distance of natural fracture from wellbore, and the point of interaction is evaluated. A novel approach for reservoir simulation is used, where porosity (instead of permeability) is used as a scaling parameter for the fracture width. The forward modeling effort, including the comparative fracture geometries setup, induced, and natural fracture interaction parametric study, is unique.

The cuttings re-injection (CRI) well in the Astokh area of Piltun-Astokhskoye field offshore Sakhalin Russia is one of the longest operating drilling waste disposal wells in the oil and gas industry worldwide. The Astokh area has been developed as a waterflood and is operated by Sakhalin Energy, a joint venture between Gazprom, Shell, Mitsui, and Mitsubishi. The Astokh CRI well has been utilized for waste injection for over 16 years. About 300,000 m3 of waste has been disposed into the main injection zone of the CRI well. Monitoring and modelling the CRI process to understand the evolution of the disposal domain is paramount for safeguarding further disposal operations. The disposal domain can be described as a complex system of multiple hydraulic and natural fractures due to injection under fracturing conditions. CRI domain evaluation includes analysis of historical injection pressures to identify the reasons of continuous injection pressure increase with increasing cumulative waste volumes disposed, to confirm domain containment, and to predict remaining domain capacity. Transient pressure analysis has revealed that the fracture closure pressure, driven by pore pressure increase and the accumulation of injected solid-phase waste, is the key parameter affecting injection pressures. Injection intensity, periods of shut-in, large overflushes, and solids-free liquids injections with corresponding solids and stresses redistribution are the other factors that affecting the pressure trends. CRI domain mapping was carried out with history-matched time-lapse 3D hydraulic fracture models. Injection pressure history matching results reveal the fracture geometry evolution during well life. The distribution of the injected liquid phase in the sand layers was modeled with a 3D dynamic reservoir sector model, matched with injection pressures and with formation pressure data in two offset wells, located at a distance of 1 and 2 kilometers, respectively. A matched model was then used to assure fracture containment for future waste disposal and to estimate remaining domain capacity. High-precision temperature and spectral noise logs were acquired in seawater injection and shut-in modes. The log-derived fracture height confirmed the domain size predicted by the matched model. 4D seismic data processing revealed that dimensions of Geomechanically Altered Rock Volume (GARV) were also in the same range as predicted by the model p. The integration of CRI domain evaluation with matched 3D hydraulic fracture models, well logs and 4D seismic demonstrated that injection pressure data collected during every injection cycle may be sufficient to characterize disposal domain evolution and to estimate domain capacity.

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.

The paper describes a fast and approximate 1D simulation algorithm for calculating the percent recovery that can be obtained from an oil reservoir if gas injection is carried out at a pressure lower than the minimum miscibility pressure. The algorithm is based on the Method of Characteristics. While a conventional 1D reservoir simulation of a gas injection scenario may take minutes or even hours, the proposed algorithm allows a full evaluation of the recovery to be completed within seconds. To make the method numerically robust, a number of approximations were needed. The result is an extremely fast algorithm that not only provides a good estimate of the recovery obtained by gas injection, but also gives a good visualization of how the gas displaces the oil.

The potential of nanoparticles, which are classified as advanced fluid material, have been unlocked for improved oil recovery in recent years such as nanoparticles-assisted waterflood process. However, there is no existing commercial reservoir simulation software that could properly model phase behaviour and transport phenomena of nanoparticles. This paper focuses on the development of a novel robust advanced simulation algorithms for nanoparticles that incorporate all the main mechanisms that have been observed for interpreting and predicting performance. The general algorithms were developed by incorporating important physico-chemical interactions that exist across nanoparticles along with the porous media and fluid: phase behaviour and flow characteristic of nanoparticles that includes aggregation, splitting and solid phase deposition. A new reaction stoichiometry was introduced to capture the aggregation process. The new algorithm was also incorporated to describe disproportionate permeability alteration and adsorption of nanoparticles, aqueous phase viscosities effect, interfacial tension reduction, and rock wettability alteration. Then, the model was tested and duly validated using several previously published experimental datasets that involved various types of nanoparticles, different chemical additives, hardness of water, wide range of water salinity and rock permeability and oil viscosity from ambient to reservoir temperature. A novel advanced simulation tool has successfully been developed to model advanced fluid material, particularly nanoparticles for improved/enhanced oil recovery. The main scripting of physics and mechanisms of nanoparticle injection are accomplished in the model and have acceptable match with various type of nanoparticles, concentration, initial wettability, solvent, stabilizer, water hardness and temperature. Reasonable matching for all experimental published data were achieved for pressure and production data. Critical parameters have been observed and should be considered as important input for laboratory experimental design. Sensitivity studies have been conducted on critical parameters and reported in the paper as the most sensitive for obtaining the matches of both pressure and production data. Observed matching parameters could be used as benchmarks for training and data validation. Prior to using in a 3D field-scale prediction in Malaysian oilfields, upscaling workflows must be established with critical parameters. For instance, some reaction rates at field-scale can be assumed to be instantaneous since the time scale for field-scale models is much larger than these reaction rates in the laboratory.

Recent papers on pre-frac tests have proposed fracture closure pressure interpretation methodologies that lead to an earlier, higher stress estimation than the ones estimated from well-established practices. These early time estimations based on the fracture compliance method lead the practitioner to utilize unrealistic permeability, stress, and fracture pressure models. This, in turn, has a severe impact on the modeled fracture geometries which hinders the hydraulic fracture optimization process. A multi-basin analysis of pre-frac tests from the North Sea, Europe, Russia, North Africa and South America is presented to support traditional closure estimation techniques. The validity of traditional minimum stress interpretation techniques will be reinforced through multiple case histories by comparing permeability estimates from the time required for the fracture to achieve closure during diagnostic injections, after-closure analysis, core, pressure build up and rate transient analysis. Results will be supported further by fiber optics and production logging tool (PLT) driven flow allocation, fracture geometry assessment through micro seismic and sonic anisotropy, and diagnostic injections numerical inversions.

Reservoir management routinely requires assimilating historical data and predicting field performance against multiple production strategies before implementing them in the field. However, traditional numerical methods are often cumbersome to characterize, build and calibrate at a timescale that can be used reliably for such short-term decision cycles such as production forecasting, IOR optimization and production rate control. Simpler analytical models make assumptions and lack the rigor needed to adequately model these systems. Pure data-driven methods may lack physical insights or have limited range of applicability. Model fidelity, speed, interpretability, suitability to automate and ease-of-use are some key modeling traits that are desired for reservoir management purposes. In this work, we propose to use a reservoir graph-network modeling approach (RGNet), based on the concept of diffusive time of flight, to forecast well performance using routinely measured field measurements (e.g. bottomhole pressure and rates). We propose a novel, model order reduction method based on discretized time of flight for multiple wells with interference. It simplifies the 3D reservoir flow problem into a flow network representation that can be solved as a 2D simulation model with any general-purpose reservoir simulator. Parameters in RGNet model cover well productivity index, grid pore volume and transmissibility, which are estimated through a history-matching process. After history matching, multiple posterior RGNet models are generated to quantify subsurface uncertainties. The RGNet modeling approach allows various fluid-flow physics to be modeled within the grids and boundary conditions, and is applicable to a range of conventional and unconventional reservoirs with different flow mechanisms. We applied the proposed approach on a field case reservoir models for multiple wells with interference. By virtue of the reduced complexity, the modeling methodology is highly scalable and still retains physical interpretability. The calibration method produces parsimonious models and provides uncertainty estimates in history matching parameters with range of outcomes. In addition, the RGNet models are much faster to simulate, over 1000x speed up, compared with full-physics models. We then used RGNet models for well-control and flood optimization and achieved significant improvement over field net-present-values. Parameterization of the proposed reservoir graph-network modeling approach provides a unique and sustainable way to reduce model complexity needed for reservoir management purposes. The method is rooted in physical principles and provides an explainable dynamic reservoir model that can be effectively used to understand reservoir behavior and optimize performance. The lightweight model lends itself naturally to fast computation that are required for scenario analysis and optimization.

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.