Summary The initial water saturation in a reservoir is important for both hydrocarbon volume estimation and distribution of multiphase flow properties such as relative permeability. Often, a practical reservoir engineering approach is to relate relative permeability to flow property regions by binning of the initial water saturation. The rationale behind this approach is that initial water saturation is related to both the pore-throat radius distribution and the wettability of the rock, both of which affect relative permeability. However, pore-throat radius and wettability are usually not explicitly included in geomodel property modeling. Therefore, the saturation height model should not only capture an average hydrocarbon pore volume but also reflect the underlying mechanisms from hydrocarbon migration history and its impact on initial water saturation distribution. This work introduces and describes a new term, excess water , for more precise classification of saturation height model scenarios in reservoirs in which multiple mechanisms have interacted and caused a complex water saturation distribution. An example of the presence of transition zones related to drained local perched aquifers (excess water) in oil-down-to (ODT) wells is shown using a limited data set from a North Sea reservoir. The physical basis for drainage and imbibition transition zones connected to both regional and perched aquifers is given. The distribution of initial water saturation in reservoirs containing excess water is demonstrated through numerical modeling of oil migration over millions of years. Highly permeable reservoirs are more likely to have locally trapped water because of lower capillary forces. A static situation occurs in areas where the capillary forces cannot maintain a high enough water saturation for further water drainage. On the other hand, both high- and low-permeability reservoirs may have significant excess water because of ongoing dynamic effects. In both cases, long distances for water to drain laterally to a regional aquifer enhance the possibility for a dynamic excess water situation.

Summary Geoscientific and engineering experiments in petrophysics, rock physics, and rock mechanics depend on multiple, costly, and sometimes rare samples used to characterize the properties of natural rocks. Testing these samples helps in modeling various hydrocarbon recovery and stimulation scenarios, as well as understanding the fluid-rock interactions in the subsurface under various pressure and temperature conditions. Over the last decade, 3D printing has matured to become a more commonly available tool to enable repeatable experiments with controllable materials and pore system geometries to investigate petrophysical, geomechanical, and geophysical properties of porous rocks. This review introduces the development, characteristics, and capabilities of 3D printing technology that are specifically used in research. Applications in the realm of petrophysics highlight the issues of replicating the pore network geometry and subsurface physics, aiming at understanding fluid flow in porous media problems. Using 3D-printed models in rock mechanics experiments focuses on generating comparable geomechanical properties and reproducing fractures, joint surfaces, and other rock structures, whereas in rock physics, geophysical forward modeling is highlighted to take advantage of 3D printing technology. By summarizing the recent advances in 3D printing as applied to petrophysics, rock physics, and rock mechanics, this review paper presents the current state of the art and the challenges in scale, cost, time, and materials, as well as the directions for advancing this frontier discipline to answer various fundamental questions regarding porous media research using 3D printing technology.

Summary Forecasting production from hydrocarbon fields is challenging because of the large number of uncertain model parameters and the multitude of observed data that are measured. The large number of model parameters leads to uncertainty in the production forecast from hydrocarbon fields. Changing operating conditions [e.g., implementation of improved oil recovery or enhanced oil recovery (EOR)] results in model parameters becoming sensitive in the forecast that were not sensitive during the production history. Hence, simulation approaches need to be able to address uncertainty in model parameters as well as conditioning numerical models to a multitude of different observed data. Sampling from distributions of various geological and dynamic parameters allows for the generation of an ensemble of numerical models that could be falsified using principal-component analysis (PCA) for different observed data. If the numerical models are not falsified, machine-learning (ML) approaches can be used to generate a large set of parameter combinations that can be conditioned to the different observed data. The data conditioning is followed by a final step ensuring that parameter interactions are covered. The methodology was applied to a sandstone oil reservoir with more than 70 years of production history containing dozens of wells. The resulting ensemble of numerical models is conditioned to all observed data. Furthermore, the resulting posterior-model parameter distributions are only modified from the prior-model parameter distributions if the observed data are informative for the model parameters. Hence, changes in operating conditions can be forecast under uncertainty, which is essential if nonsensitive parameters in the history are sensitive in the forecast.

Summary Carbon dioxide (CO 2 ) enhanced oil recovery (EOR) has long been practiced in the US as an efficient mean for enhancing oil production. Many of the US CO 2 -EOR developments have been designed horizontally. This is because of a viscous-dominated CO 2 flow regime that is prevalent in these developments driven by thin and low-permeability reservoirs. Reservoirs and fluid properties are different in the North Sea. Pays are usually thicker with better petrophysical properties. Lighter oils can also be found in North Sea reservoirs. This suggests that a dissimilar flow regime might prevail CO 2 displacements in the North Sea developments, which could favor a dissimilar CO 2 -EOR process design. This study thus compares CO 2 flow regimes between several North Sea and US reservoirs. We use scaling analysis to characterize and compare CO 2 flow regimes between these two classes of reservoirs. Scaling analysis characterizes CO 2 displacement in each reservoir system using three dimensionless numbers: gravity, effective aspect ratio, and mobility ratio. Displacement experiments conducted in stochastically generated permeability fields, under exactly matched magnitudes of the derived dimensionless numbers, reveal the prevailing CO 2 flow regime in each reservoir system. Results of scaling analysis indicate that CO 2 flooding in the North Sea reservoirs can be generally characterized with a larger gravity number, smaller effective aspect ratio, and smaller mobility ratio than the average US CO 2 flooded reservoirs. Flow regime analysis indicates that unlike the majority of the US CO 2 flooded reservoirs, CO 2 flow regimes tend to be more gravity-dominated in the North Sea class of reservoirs. CO 2 flow regimes in the North Sea systems are expected to suffer from a higher degree of instability because of thicker North Sea pays, which limit effective crossflow. Understanding the differences and characteristics of CO 2 flow regimes in the North Sea prospects can help operators design their CO 2 flooding more efficiently, which could increase the recovery factor (RF) as well as address CO 2 storage requirements, a necessary consideration for CO 2 -EOR deployment in the North Sea.

Summary Storage of large amounts of carbon dioxide (CO 2 ) within deep underground aquifers has great potential for long‐term mitigation of climate change. The U.S. Gulf Coast is an attractive target for CO 2 storage because of the favorable formation properties for injection and containment of CO 2 . Deltaic formations are one of the primary targeted depositional environments in the Gulf Coast. In this paper, we investigate CO 2 storage in deltaic saline aquifers through a combination of geological modeling and flow simulation. The approach presented in this paper based on a combination of invasion percolation and compositional reservoir simulation focuses on buoyancy‐dominant flow of CO 2 in the long term and its pressure‐driven flow in the short term. The results provide insights into how to screen and identify prospective locations for CO 2 storage and determine the underlying geological features controlling CO 2 migration for a basin‐scale study. The geological model in our study is developed based on a laboratory‐scale three‐dimensional (3D) flume experiment replicating the formation of a delta structure and populated with geologic properties according to Miocene Gulf of Mexico natural analogues. We used invasion percolation simulations to understand the buoyancy‐driven flow and the relationship between architecture, stratigraphy, and fluid migration pathways. The results were used to develop an upscaled model for compositional simulation with the key features of the original geological model and to determine injection schemes that maximize the injection capacity and minimize the amount of mobile CO 2 in the formation. To achieve this, we used compositional reservoir simulations to study the pressure‐driven flow and phase behavior. The results of invasion percolation simulations were used to identify the key stratigraphic units affecting CO 2 migration. The realistic geometries and high resolution of the model facilitate the transfer of results from synthetic to subsurface data. The results allow for the analysis of deltaic depositional environments, important stratigraphic surfaces, and their impact on CO 2 storage. The reservoir simulation model and phase behavior were validated against available field and laboratory data. The results of reservoir simulations were used to investigate the effects of main mechanisms, such as gas trapping and solubilization, on storage capacity. We compared our simulation results based on invasion percolation (buoyancy driven) and reservoir simulation (pressure driven). The comparison is helpful to understand the strengths and weaknesses of each approach and determine best practices to evaluate CO 2 migration within similar formations. The unique and extremely well‐characterized deltaic model allows for unprecedented representation of the depositional aquifer architecture. This research combines geologic modeling, flow simulation, and application for CO 2 storage. The integrated conclusions will constrain predictions of actual subsurface flow performance and CO 2 storage capacity in deltaic systems while identifying potential risks and primary stratigraphic migration pathways. This research provides insights on prediction of CO 2 storage performance and characterization of prospective saline aquifers.

Summary Capturing the correct reservoir heterogeneity in a geological model is critical for designing and accurately forecasting expected production benefits from improved/enhanced oil recovery processes. In simple terms, reservoir heterogeneity is often considered as a measure of statistical variation of static properties, such as porosity and permeability. The Lorenz coefficient and Dykstra-Parsons coefficient are two such measures of reservoir heterogeneity that account for these static effects. These measures are considered simplistic because the spatial distribution and arrangement of these properties is more critical for reservoir characterization than its statistical variation. The dynamic Lorenz coefficient is one such measure that accounts for the spatial distribution and arrangement of porosity and permeability. The dynamic Lorenz coefficient cannot be directly measured in the field but can be implicitly inferred from tracer data. This inferred heterogeneity will be influenced by the spatial distribution of static properties and can also be significantly influenced by multiphase flow effects, such as viscous fingering and gravity over/underride, which often arise from the differences in the viscosities and densities of the different phases involved. The dynamic Lorenz coefficient interpreted from a tracer test response lumps both static and multiphase (dynamic) effects into one measure of heterogeneity. During geological model construction, the dynamic Lorenz coefficient is also used to rank the model in terms of heterogeneity to select the most representative model. However, the geological models are ranked using fast single-phase/streamline simulations and are devoid of complications caused by adverse mobility or density effects. This creates a disconnect between the heterogeneity measures used to characterize a geological model and those available to characterize a reservoir. Thus, calibrating a geological model to field data is a laborious task. In this paper, we present a workflow that bridges this gap by decoupling the effect of adverse mobility to obtain an approximate measure of heterogeneity that can be cross-checked against the geological realizations to select the most representative model. The workflow developed in this paper is for inverted, seven-spot patterns and is mainly focused on water-wet reservoirs.

Summary Wettability is an important petrophysical property, which governs irreducible fluid saturations, relative permeability, and fluid invasion in rocks. Unlike conventional reservoirs, which have relatively uniform pore surface properties, the concept of wettability is not clear in organic-rich tight reservoirs. These rocks do not only have a nanoporous system but also possess multiple pore types with different interfacial affinities. Previous studies have shown that the unconventional reservoirs consist of three major pore types: inorganic pores (assumed to be water-wet), organic pores (assumed to be oil-wet, controlled by organic matter and thermal maturity), and mixed-wet pores (controlled by organic-inorganic distribution) ( Curtis et al. 2012 ). This study revisits the concept of pore-type partitioning in tight rocks. We propose and demonstrate a new workflow to evaluate pore partitioning. First, all the specimens were vacuum dried at 100°C for 6 days to remove the free fluids until the weight stabilized. Total porosity was estimated as the sum of residual liquid volume [using nuclear magnetic resonance (NMR)] and gas-filled volume [using helium high-pressure pycnometer (HPP)]. The companion specimens from two formations (Eagle Ford and Wolfcamp B) were subjected to multiple injection cycles: starting with imbibition, then counter imbibition, and finally step pressurization with the replacing phase. During this process, we used brine-then-dodecane and dodecane-then-brine as the injection fluid sequences. The companion samples were continuously monitored by both gravimetric and NMR measurements until equilibration. Relative fractions of both replaced and replacing phases were calculated from sample weights and pore-fluid volumes. The new approach not only classifies the connected pore network into three categories—oil-wet, water-wet, and mixed-wet—but also quantifies their respective proportions. The mixed-wet pore is defined as the pore fraction, in which both oil and water can replace air under capillary suction. We observe that the behavior of mixed-wet pores is different among formations: they prefer brine over oil in Wolfcamp B shale, while they prefer oil over brine in the Eagle Ford Formation. Unlike conventional wettability assessments, from which an overall wettability is provided, the novelty of this method is to clearly classify different pore types and their distributions. The concept of fractional wettability is highlighted for organic-rich tight rocks, and the contribution of mixed-wet pores in relative flow is emphasized. Hydraulic fracturing is a necessary stimulation process for tight reservoirs, in which the usage of fracturing fluid can affect formation performance. During well completion, water blockage is more likely to happen in the Wolfcamp B Formation than Eagle Ford Formation. Due to the capillary preference of mixed-wet pores, the water blockage might aid the Wolfcamp Formation to boost initial production, however, at a later stage, damage the formation. The workflow thus is promising to fully describe the pore network in tight formations, in which pore-type partitioning is a more reasonable concept than wettability.

Summary The extensive depletion of the development target triggers the demand for infill drilling in the upside target of multilayer unconventional reservoirs. However, such an infill scheme in the field practice still heavily relies on empirical knowledge or pressure responses, and the geomechanics consequences have not been fully understood. Backed by the data set from the Permian Basin, in this work we present a novel integrated reservoir-geomechanics-fracture model to simulate the spatiotemporal stress evolution and locate the optimal development strategy in the upside target of the Bone Spring Formation. An embedded discrete fracture model (EDFM) is deployed in our fluid-flow simulation to characterize complex fractures, and the stress-dependent matrix permeability and fracture conductivity are included through the compaction/dilation option. After calibrating reservoir and fracture properties by history matching of an actual well in the development target (i.e., third Bone Spring), we run the finite element method (FEM)-based geomechanics simulation to model the 3D stress state evolution. Then a displacement discontinuity method (DDM) hydraulic fracture model is applied to simulate the multicluster fracture propagation under an updated heterogeneous stress field in the upside target (i.e., second Bone Spring). Numerical results indicate that stress field redistribution associated with parent-well production indeed vertically propagates to the upside target. The extent of stress reorientation at the infill location mainly depends on the parent-child horizontal offset, whereas the stress depletion is under the combined impact of horizontal offset, vertical offset, and infill time. A smaller parent-child horizontal offset aggravates the overlap of the stimulated reservoir volume (SRV), resulting in more substantial interwell interference and less desirable oil and gas production. The same trend is observed by varying the parent-child vertical offset. Moreover, the efficacy of an infill operation at an earlier time is less affected by parent-well depletion because of the less-disturbed stress state. The candidate infill-well locations at various infill timings are suggested based on the parent-well and child-well production cosimulation. Being able to incorporate both pressure and stress responses, the reservoir-geomechanics-fracture model delivers a more comprehensive understanding and a more integral solution of infill-well design in multilayer unconventional reservoirs. The conclusions provide practical guidelines for the subsequent development in the Permian Basin.

Summary Nonequilibrium foamy oil behavior and solvent transport are two important recovery mechanisms for cyclic solvent injection (CSI) processes in post-cold heavy oil production with sand (CHOPS) reservoirs. The nonequilibrium solvent exsolution and gas bubbles generated during the pressure depletion stage have the typical characteristics of foamy oil flow. In this paper, a field-scale post-CHOPS model is constructed and upscaled from a core model, which was calibrated against detailed experimental data involving various propane (C 3 H 8 )-based and carbon dioxide (CO 2 )-based solvent mixtures. The field model is upscaled from the core model to analyze the impacts of simulation scales, heterogeneous wormholes, and the operating schedules on foamy oil behavior of different solvent systems. Reaction kinetics are implemented to represent the nonequilibrium gas dissolution and exsolution for foamy oil flow. A fractal wormhole network is modeled. To analyze the impacts of pressure depletion strategies, single-stage pressure depletion involving three oil solvent systems, as well as two cycles of CSI production processes, are examined. Detailed sensitivity analyses involving different solvent compositions are discussed. The results illustrate that both C 3 H 8 -based and CO 2 -based solvents exhibit significant nonequilibrium foamy oil characteristics, enabling the oil viscosity to remain close to its value with dissolved solvent during the pressure depletion process. However, the amount of nonequilibrium foamy oil flow is strongly dependent on the pressure depletion rate: A faster depletion rate is beneficial for higher oil recovery. The core model results are more sensitive to the solvent types, whereas the field-scale simulations show comparable recovery performance for both C 3 H 8 -based and CO 2 -based solvents. This observation highlights the significance of domain size, time scale, and wormhole heterogeneities on the ensuing foamy oil behavior. Although several post-CHOPS models were developed in the past, detailed field-scale models that simulate nonequilibrium foamy oil kinetics in a realistic wormhole network are lacking. The simulation model developed here has been calibrated against detailed experimental measurements and upscaled from a core-scale model. Improving our understanding of solvent dissolution/exsolution would aid in the design of operating strategies (e.g., pressure depletion and solvent injection schemes) for enhanced solvent/oil mixing and transport.

Summary While there are a number of mechanistic foam models available in the literature, it still is not clear how such models can be used to guide actual field development planning in enhanced oil recovery (EOR) applications. This study aims to develop the framework to determine the optimum injection condition during foam EOR processes by using a mechanistic foam model. The end product of this study is presented in a graphical manner, based on the sweep-efficiency contours (from reservoir simulations) and the reduction in gas mobility (from mechanistic modeling of foams with bubble population balance). The main outcome of this study can be summarized as follows: First, compared to gas/water injection with no foams, injection of foams can improve cumulative oil recovery and sweep efficiency significantly. Such a tendency is observed consistently in a range of total injection rates tested (low, intermediate, and high total injection rates Q t ). Second, the sweep efficiency is more sensitive to the injection foam quality f g for dry foams, compared to wet foams. This proves how important bubble-population-balance modeling is to predict gas mobility reduction as a function of Q t and f g . Third, the graphical approach demonstrates how to determine the optimum injection condition and how such an optimum condition changes at different field operating conditions and limitations (i.e., communication through shale layers, limited carbon dioxide (CO 2 ) supply, cost advantage of CO 2 compared to surfactant chemicals, etc.). For example, the scenario with noncommunicating shale layers predicts the maximum sweep of 49% at f g = 55% at high Q t , while the scenarios with communicating shale layers (with 0.1-md permeability) predicts the maximum sweep of only 40% at f g = 70% at the same Q t . The use of this graphical method for economic and business decisions is also shown, as an example, to prove the versatility and robustness of this new technique.

Summary Reduction of fracture/well spacing and increases in hydraulic fracture stimulation treatment size are popular strategies for improving hydrocarbon recovery from multifractured horizontal wells (MFHWs). However, these strategies can also increase the chance of fracture interference, which can not only negatively impact the overall production but also introduce complexities for production data analysis. A semianalytical model is therefore developed to analyze production data from two communicating MFHWs and applied to a field case. The new semianalytical model uses the dynamic drainage area (DDA) concept and assumes three porosity regions. The three-region model is comprised of a primary hydraulic fracture (PHF), an enhanced fractured region (EFR) adjacent to the PHF, and a nonstimulated region (NSR). Assuming a well pair primarily communicates through PHFs, the equations for two communicating wells are coupled and solved simultaneously to model the fluid transfer between the wells. This method is used within a history-matching framework to estimate the communication between the wells by matching the production data. The semianalytical model is first verified against a more rigorous, fully numerical simulation model for a range of fracture/reservoir properties. These comparisons demonstrate that there is excellent agreement between the fully numerical simulation model results and the new semianalytical model. The semianalytical model is then employed to history-match production data from six MFHWs (drilled from two adjacent well pads) exhibiting different degrees of communication. For the purpose of history matching the data, only strong communication between pairs of wells (intrapair communication) is considered in the three-region model, and the results show good agreement with the field data. A flexible, yet simple, semianalytical model is developed for the first time that can accurately model the communication between multiple well pairs. This approach can be used by reservoir engineers to analyze the production data from communicating MFHWs.

Summary The knowledge of the effects of instability and heterogeneity on displacements, primarily enhanced oil recovery, and carbon dioxide storage are well known, although they remain difficult to predict. The usual recourse to modeling these effects is through numerical simulation. Simulation remains the gold standard for prediction; however, its results lack generality, being case-specific. There are also several analytic models for displacements that are usually more informative than simulation results. However, these methods apply to steady-state, incompressible flow. Carbon dioxide injection for storage uses compressible fluids and, in the absence of producers, will not approach steady-state flow (Wu et al. 2017). Consequently, it is unlikely that storage will be in reservoirs of open boundaries (steady-state flow). Flow of compressible fluid necessitates the use of closed or partially sealed boundaries, a factor that is consistent with compressible flow. This work deals with the conditions that cause the onset of incipient viscous fingering or Saffman-Taylor (ST) instability. The actual growth and propagation of fingers, a subject of much recent literature, is not discussed here. The original ST formalism of M > 1 for gravity-free flow is highly restrictive: it is for linear flow of nonmixing incompressible fluids in steady-state flow. In this work, we relax the incompressible flow restriction and thereby broaden the ST criterion to media that have sealing and/or partially sealing outer boundaries. We use the nonlinear partial differential equation for linear flow and developed analytic solutions for a tracer flow analog and also for a two-fluid compressible flow. The analysis is restricted to stabilized flow and to constant compressibility fluids, but we are not restricted to small compressibility fluids. There is no transition (mixing) zone between displacing and displaced fluids; the displacement is piston-like. The absence of a transition zone means that the results apply to both miscible and immiscible displacements, absent dispersion, or local capillary pressure. The assumption of a sharp interface is to focus on the combined effect of mobility ratio and compressibility. We use the product of the fluid compressibility and pressure drop ( c f Δ P ) to differentiate the compressibility groups ( Dake 1978 ; Dranchuk and Quon 1967 ), where Δ P is defined as the pressure drop within the specific fluid region. The results will be based on proposed analytical solutions compared to numerical simulation. The proposed formulation is less restrictive than the original ST formalism of M > 1 and allows evaluation of viscous fingering initiation or ST stability criterion in the presence of different boundary conditions (open vs. closed boundaries) with compressible fluids under the stated assumptions, which is the scope of this work. The key contribution here is the effect of external boundaries, which consequently makes necessary the use of compressible fluids. Absent compressibility, the necessary condition for the growth of a viscous finger is simply the mobility ratio, M > 1. It is the objective of this work to study how the ST criterion is affected by the presence of sealing and partially sealing outer boundaries with the consequent inclusion of compressible flows as in carbon dioxide storage and enhanced oil recovery by gas injection. The results show that adding compressibility always makes displacements more unstable for stabilized background flow, even for a favorable mobility ratio ( M < 1) at extremely large compressibility (e.g., c f > 5×10 −3 1/psi). For a sealed external boundary (no production or leakage), displacements will become more stable as a front approaches an external boundary for all mobility ratios ( M ) investigated.

Summary The first pilot test of a surfactant/polymer (SP) flood in the world with mixtures of anionic/cationic surfactants ( S a / c ) was performed in a high-temperature and low-permeability sandstone reservoir with a high content of clay to demonstrate the potential of this novel technique to improve oil recovery. Low critical micelle concentration of 2.78×10 −6 mol/L, ultralow interfacial tension (IFT) of 10 −3 to 10 −4 mN/m when surfactant concentrations were greater than 0.025 wt%, and lower phase microemulsion with solubilization ratio of 22, as well as oil-washing rate of 61%, were obtained by using S a / c . Carboxymethylcellulose sodium was adopted as an adsorption inhibitor (AI) to reduce the adsorption because of the high clay contained in the natural core. With the addition of AI, dynamic adsorption of S a / c was approximately 0.30 mg/g sand, and the IFT remained nearly unchanged before and after adsorption. The viscosities of polyacrylamide with molecular weight (MW) of 6.16×10 6 daltons and SP were 3.0 and 4.1 mPa·s, respectively. Coreflooding results showed that more than 16% of original-oil-in-place (OOIP) crude oil was recovered by SP over waterflood. The pilot test of SP containing S a / c was performed in a Sinopec reservoir with temperature of 83°C, salinity of 1.69%, permeability of 41.5 md, and clay content of 10 to 16%. A total of 0.40-pore volume (PV) chemicals, including 0.1-PV polymer preslug, 0.25-PV SP main slug, and 0.05-PV post-polymer drive, were injected from August 2012 to December 2017 with an injection rate of 0.08 PV/yr. Then, waterdrive was conducted. Maximal water cut decreased from 82.2 to 62.1%, and the peak daily oil production increased from 12.2 to 32.3 t. The oil recovery was increased by 8.0% of OOIP within 4 years.

Summary The main objective of this work is to improve robust, repeatable interpretation of reservoir characteristics using rate transient analysis (RTA). This is to generate probabilistic credible intervals for key reservoir and completion variables. This resulting data-driven algorithm was applied to production data from both synthetic and actual case histories. Synthetic production data from a multistage, hydraulically fractured horizontal completion in a reservoir modeled after the Marcellus Shale reservoir were generated using a reservoir model. The synthetic production data were analyzed using a combination of RTA and Bayesian techniques. First, the traditional log-log plot was produced to identify the linear flow production regime. Using the linear flow production data and traditional RTA equations, Bayesian inversion was carried out using two distinct Bayesian methods. The “rjags” and “EasyABC” packages in the open-source statistical software R were used for the traditional and approximate inversion, respectively. Model priors were based on (1) information available about the Marcellus Shale from technical literature and (2) results from a hydraulic fracturing forward model. Posterior distributions and credible intervals were produced for the fracture length, matrix permeability, and skin factor. These credible intervals were then compared with true reservoir and hydraulic fracturing data. The methodology was also repeated for an actual case in the Barnett shale. The most substantial finding was that for nearly all the investigated cases—including complicated scenarios (such as including finite fracture conductivity, fracturing fluid flowback, and heterogeneity in fracture length in the reservoir/hydraulic fracturing forward model)—the combined RTA-Bayesian model provided a 95% credible interval that encompassed the true values of the reservoir/hydraulic fracture parameters. We also found that the choice of the prior distribution did not affect the posterior distribution/credible interval in a significant manner as long as it was moderately concentrated and consistent with engineering science. Also, a comparison of the approximate Bayesian computation (ABC) and the traditional Bayesian algorithms showed that the ABC algorithm reduced computational time by at least an order of magnitude with minimal loss in accuracy. In addition, the production history used, the number of iterations, and the tolerance of fitting in the ABC analysis had a minimal impact on the posterior distribution after an optimal point, which were determined to be at least 1-year production history, 10,000 iterations, and 0.001, respectively. In summary, the RTA-Bayesian production analysis method was implemented using relatively user-friendly computational platforms [ R and Excel® (Microsoft Corporation, Redmond, Washington, USA)]. This methodology provided reasonable characterization of all key variables such as matrix permeability, fracture length, and skin when compared to results obtained from analytical methods. This probabilistic characterization has the potential to enable better understanding of well performance ranges expected from shale gas wells. The methodology described here can also be generalized to shale oil systems during linear flow.

Summary Two of the most critical properties for multiphase flow in a reservoir are relative permeability ( K r ) and capillary pressure ( P c ). To determine these parameters, careful interpretation of coreflooding and centrifuge experiments is necessary. In this work, a machine learning (ML) technique was incorporated to assist in the determination of these parameters quickly and synchronously for steady-state drainage coreflooding experiments. A state-of-the-art framework was developed in which a large database of K r and P c curves was generated based on existing mathematical models. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from the corefloods including pressure drop and water saturation profile, along with other conventional core analysis data, were fed as features into the ML model. The entire data set was split into 70% for training, 15% for validation, and the remaining 15% for the blind testing of the model. The 70% of the data set for training teaches the model to capture fluid flow behavior inside the core, and then 15% of the data set was used to validate the trained model and to optimize the hyperparameters of the ML algorithm. The remaining 15% of the data set was used for testing the model and assessing the model performance scores. In addition, K -fold split technique was used to split the 15% testing data set to provide an unbiased estimate of the final model performance. The trained/tested model was thereby used to estimate K r and P c curves based on available experimental results. The values of the coefficient of determination ( R 2 ) were used to assess the accuracy and efficiency of the developed model. The respective crossplots indicate that the model is capable of making accurate predictions with an error percentage of less than 2% on history matching experimental data. This implies that the artificial-intelligence- (AI-) based model is capable of determining K r and P c curves. The present work could be an alternative approach to existing methods for interpreting K r and P c curves. In addition, the ML model can be adapted to produce results that include multiple options for K r and P c curves from which the best solution can be determined using engineering judgment. This is unlike solutions from some of the existing commercial codes, which usually provide only a single solution. The model currently focuses on the prediction of K r and P c curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

Summary The nonparametric transformation is a data-driven technique, which can be used to estimate optimal correlations between a dependent variable (response) and a set of independent parameters (predictors). This study introduces a systematic methodology using the nonparametric transformation concept and the alternating conditional expectation (ACE) algorithm to estimate the effective gas permeability using conventional logs and the core data. The ACE algorithm was employed in the current work using the MATLAB® (The MathWorks, Inc., Natick, Massachusetts, USA) code and the open-source GRaphical ACE (GRACE) software (Xue et al. 1997) for deriving the optimal nonparametric correlations for predicting the permeability. The methodology was applied to a heterogeneous formation [Bahariya (BAH)] in Egypt to understand its characteristics and predict its permeability more accurately. The BAH Formation is considered one of the main sources for oil production throughout the Western Desert (WD) of Egypt. The cumulative oil production from the BAH Formation is estimated to be approximately 40% of the total WD production. The reservoir characteristics of the BAH Formation range from highly permeable to tight sandstone interbedded with shale and siltstone. It usually depicts low-resistivity and low-contrast (LRLC) log behavior. Thus, regional and accurate determination of the reservoir permeability for the different rock units of the BAH Formation across the WD is a challenge. Conventional well log data from approximately 100 cored wells and corresponding 5,500 core measurements were used to provide a regional permeability correlation that can be used in a large number of reservoirs. The methodology of this work included two main steps: Applying the nonparametric transformation technique to identify the collective log responses for deriving optimal correlation Predicting the permeability profiles using the selected log responses The model was applied to many wells that address different petrophysical characteristics of the BAH Formation. The established permeability profiles showed reliable correlation coefficients relative to the measured core data. The correlation coefficient was 0.893 for the training data points (75% of the collected database) and 0.913 for the testing data points (25% of the collected database). In addition, the mean absolute percentage error (MAPE) between the predicted and the measured permeability for the training and testing data points were 5.93 and 4.14%, respectively. Permeability prediction using ACE is compared with other techniques such as k-ϕ crossplots, multiple linear regression (MLR), Coates, and Wyllie-Rosecorrelations. This work is considered an original contribution to present regional permeability prediction correlations using the conventional well logs for reservoir characterization and simulation applications. The ACE algorithm was successfully applied to the BAH Formation and proved its capability to identify the best predictors that are required to establish a rigorous model.

Summary It is common to produce some percentage of water during the oil‐extraction process. Conventionally, some water‐disposal wells are drilled in an oil field to inject these useless and hazardous waters. Mineral scale formation is a critical issue in water‐injection wells and may result in well plugging and an injection rate decrease in these wells. The two steps of mineral scale formation are scale precipitation and scale deposition. Two main mechanisms of inorganic scale precipitation are incompatibility between injected water and reservoir formation water and changes in the thermodynamic state of injected water. The injectivity of the well decreases because of deposition of supersaturated precipitated scales through the well column and near‐wellbore region. Currently, limited research has been done to evaluate inorganic scale deposition, and most of the research is limited to calculation of total scaling by commercial software. In this study, the mineral scale precipitation is evaluated by software modeling and laboratory experiments in an Iranian oil field, and the effect of the scale deposition phenomenon is assessed on permeability impairment and injection rate decrease. One of the major novelties of this work is simulation of various scale‐deposition models by coupling MATLAB® software coding and a reservoir simulator. The accuracy of different deposition models is analyzed by comparing them with field data (real water‐injection well) and laboratory tests (coreflooding test). Finally, our simulation results show that a single deposition model could not exactly predict the scaling phenomena in the studied carbonate reservoir that is supersaturated with CaCO 3 and CaSO 4 . It is recommended to improve the scale‐formation prediction with a mixed deposition model supported by reliable static/dynamic modeling and experimental analysis.

Summary Seawater injection is widely used to maintain offshore-oil-reservoir pressure and improve oil recovery. However, injecting seawater into reservoirs can cause many issues, such as reservoir souring and scaling, which are strongly related to the seawater-breakthrough percentage. Accurately calculating the seawater-breakthrough percentage is important for estimating the severity of those problems and further developing effective strategies to mitigate those issues. The validation of using natural-ion boron as a tracer to calculate seawater-breakthrough percentage was investigated. Boron can interact with clays, which can influence the accuracy in seawater-breakthrough calculation. Therefore, the interaction between boron and different clays at various conditions was first studied, and the Freundlich adsorption equation was used to describe the boron-adsorption isotherms. Then, the boron-adsorption isotherms were coupled into the reservoir simulator to investigate the boron transport in porous media, and the results in turn were further analyzed to calculate the accurate seawater-breakthrough percentage. Results indicated that boron adsorption by different clays varied. pH value of solution can significantly influence the amount of boron adsorbed. As a result, the boron-concentration profile was delayed in coreflood tests. The accuracy of the new model was verified by convergence rate tests and comparison with analytical results. Furthermore, model results fit well with experimental data. On the basis of the reservoir-simulation results, the boron-concentration profile in produced water can be used to calculate the seawater-breakthrough percentage by considering the clay-content distribution. However, the seawater-breakthrough point cannot be determined by boron because the boron concentration is still at the formation level after seawater breakthrough due to boron desorption.

Summary A potential enhanced oil recovery technique is to inject alkali into a reservoir with a high-total acid number (TAN) crude to generate soap in situ and reduce interfacial tension (IFT) without the need to inject surfactant. The method may be cost-effective if the IFT can be lowered enough to cause significant mobilization of trapped oil while also avoiding formation of gels and viscous phases. This paper investigates the potential field application of injecting alkali to generate in-situ soap and favorable phase behavior for a high-TAN oil. Oil analyses show that the acids in the crude are a complex mixture of various polar acids and not mainly carboxylic acids. The results from phase behavior experiments do not undergo typical Winsor microemulsion behavior transition and subsequent ultralow IFTs below 1×10 −3 mN/m that are conventionally observed. Instead, mixing of alkali and crude/brine generate water-in-oil macroemulsions that can be highly viscous. For a specific range of alkali concentrations, however, phases are not too viscous, and IFTs are reduced by several orders of magnitude. Incremental coreflood recoveries in this alkali range are excellent, even though not all trapped oil is mobilized. The viscous phase behavior at high alkali concentrations is explained by the formation of salt-crude complexes, created by acids from the crude oil under the alkali environment. These hydrophobic molecules tend to agglomerate at the oil-water interface. Together with polar components from the crude oil, they can organize into a highly viscous network and stabilize water droplets in the oleic phase. Oil-soluble alcohol was added to counter those two phenomena at large concentrations, but typical Winsor phase behavior was still not observed. A physicochemical model is proposed to explain the salt-crude complex formation at the oil-water interface that inhibits classical Winsor behavior.

Summary The heavy-oil- and bitumen-recovery process by injection of a pure condensing solvent in a solvent vapor chamber provides an alternative to steam-based recovery techniques such as steam-assisted gravity drainage (SAGD). Because of the lower operating temperature between 40 and 80°C, the process uses a much lower energy budget than a steam process and thus results in significantly reduced greenhouse-gas emissions. This paper describes the route to a successful production function with the physical processes at play and using analytical tools. Physical relationships are derived for the solvent/bitumen (S/B) ratio, the bitumen drainage from the roof of the solvent vapor chamber, and for bitumen extraction from both sides of the solvent chamber by the draining condensed solvent. The fast diffusion of bitumen into this narrow liquid solvent zone is likely subtly enhanced by transverse dispersion. The speed of bitumen extraction from the roof of the solvent vapor chamber is constrained by the gas/oil capillary pressure. Extraction from the side of the chamber is approximately three times faster by the action of the thin gravity-draining liquid solvent film. Several equations are provided to enable creation of a heat balance for this condensing solvent process. Laboratory and field observations are matched, including the rates, the heat balance, and the S/B ratio. The model can explain constrained production performance by identifying the rate-limiting steps (e.g., when insufficient solvent condenses). The model predicts high solvent holdup during the rise of the solvent chamber. A method to estimate this solvent liquid saturation is provided. The S/B ratio depends on injector-wellbore heat losses, the (high) liquid saturation in the rising solvent chamber, and the process properties (operating temperature), reservoir properties (heat capacity, porosity, and oil saturation), and solvent properties (density and latent heat). In the existing body of literature, no satisfactory analytical model was available; this new approach helps to constrain production performance and to estimate solvent and heat requirements. The methods in this paper can be used in the future for subsurface project design and performance predictions.