• Energy Research

Summary The steam alternating solvent (SAS) process involves multiple cycles of steam and solvent (e.g., propane) injected into a horizontal well pair to produce heavy oil. These solvent-based methods entail a smaller environmental footprint with reduced water usage and greenhouse gas emissions. However, the lack of understanding regarding the influences of reservoir heterogeneities, such as shale barriers, remains a significant risk for field-scale predictions. Additionally, the proper design of the process is challenging because of the uncertain heterogeneity distribution and optimization of multiple conflicting objectives. This work develops a novel hybrid multiobjective optimization (MOO) workflow to search a set of Pareto-optimal operational parameters for the SAS process in heterogeneous reservoirs. A set of synthetic homogeneous 2D is constructed using data representative of the Cold Lake reservoir. Next, multiple heterogeneous models (realizations) are built to incorporate complex shale heterogeneities. The resultant set of SAS heterogeneous models is subjected to flow simulation. A detailed sensitivity analysis examines the impacts of shale barriers on SAS production. It is used to formulate a set of operational/decision parameters (i.e., solvent concentration and duration of solvent injection cycles) and the objective functions (cumulative steam/oil ratio and propane retention). The nondominated sorting genetic algorithm II (NSGA-II) is applied to search for the optimal decision parameters. Different formulations of an aggregated objective function, including average, minimum, and maximum, are used to capture the variability in objectives among the multiple realizations of the reservoir model. Finally, several proxy models are included in the hybrid workflow to evaluate the defined objective functions to reduce the computational cost. Results of the optimization workflow reveal that both the solvent concentration and duration of the solvent injection in the early cycles have significant impacts. It is recommended to inject solvent for longer periods during both the early and late SAS stages. It is also noted that cases with higher objective function values are observed with more heterogeneities. This work offers promising potential to derisk solvent-based technologies for heavy oil recovery by facilitating more robust field-scale decision-making.

Abstract While compositional simulators are available for modeling the recovery performance of Vapex process, computational constraints often preclude detailed numerical solution of the flow and transport differential equations, as implemented in traditional flow simulators, using an entire suite of geostatistical realizations that represent the uncertainty due to reservoir heterogeneity occurring at different scales. Proxy models that are based on analytical formulations provide efficient alternatives to expensive detailed flow simulations. A novel semi-analytical proxy has been developed to model solvent transport in Vapex at isothermal conditions. Detailed analytical formulations were derived and implemented in a calculation procedure to advance the solvent–oil interface and estimate producing oil rate with time. A mass penetration parameter was formulated, and its change with time was tracked. Results obtained from the proxy were validated against experimental data available in the literatures as well as detailed compositional simulation studies ( Shi and Leung, 2014a , Shi and Leung, 2014b ). Later, a suite of geostatistical realizations are ranked based on their cumulative oil production using this proxy, and the results demonstrate good agreement with those based on detailed compositional simulations but with significant savings in computational costs. Finally, this proxy is employed to assess impacts of uncertainty in subscale heterogeneity. An important contribution from this work is that process physics are built directly into this proxy; it represents an advantage over other alternative modeling approaches (i.e., regression) that are driven only by data. This proposed proxy can be easily integrated in existing reservoir management workflows to optimize production scenarios in a quick and robust manner. It can be applied to rank numerous geostatistical realizations and quickly identify a smaller, more manageable subset of realizations for further simulation analysis. It presents an important tool for assessing the uncertainty in reservoir properties on effective mass transfer and the ensuing recovery performance, as well as assisting decisions-making for future pilot and field development planning.

Abstract Warm solvent injection (WSI) has been proposed as a promising alternative to steam-based methods for bitumen recovery, due to its potential to reduce greenhouse gas emissions and environmental footprint. It involves injecting heated vaporized solvent to reduce the viscosity of bitumen via solvent diffusion and latent heat transfer. The presence of reservoir heterogeneity caused by shale barriers is a severe concern for the success of the WSI because the conformance of solvent chamber advancement can be compromised. However, the efficient estimation and tracking methods for solvent chamber growth and propagation in heterogeneous reservoirs have not been widely investigated in the past. To fill this gap, this work proposes a novel machine learning-based approach to efficiently track solvent chamber positions in heterogeneous reservoirs for the WSI process. From a large training dataset consisting of numerous synthetic heterogeneous models and their simulation results, the input features and output parameters are extracted from oil production time-series data and the dynamic evolution of solvent chamber, respectively. A convolutional neural network (CNN) is implemented to dynamically track solvent chamber positions by correlating the extracted inputs and outputs. The estimation results are reliable and accurate for both scenarios where the shale barriers are either regularly or irregularly shaped with high conformance index (CI). Only production data is used to assess the conformance of solvent chamber advancement, which is an important consideration in operations design and real-time optimization. The presented workflow offers a novel alternative to infer the development of solvent chambers in heterogeneous reservoirs from production time-series data directly. This type of analysis could complement many existing monitoring techniques to deliver a more comprehensive inference of the distribution of shale heterogeneities in solvent-based bitumen recovery operations.

Production forecast of steam-assisted gravity drainage (SAGD) in heterogeneous reservoir is important for reservoir management and optimization of development strategies for oil sand operations. In this work, artificial intelligence (AI) approaches are employed as a complementary tool for production forecast and pattern recognition of highly nonlinear relationships between system variables. Field data from more than 2000 wells are extracted from various publicly available sources. It consists of petrophysical log measurements, production and injection profiles. Analysis of a raw dataset of this magnitude for SAGD reservoirs has not been published in the literature, although a previous study presented a much smaller dataset. This paper attempts to discuss and address a number of the challenges encountered. After a detailed exploratory data analysis, a refined dataset encompassing ten different SAGD operating fields with 153 complete well pairs is assembled for prediction model construction. Artificial neural network (ANN) is employed to facilitate the production performance analysis by calibrating the reservoir heterogeneities and operating constraints with production performance. The impact of extrapolation of the petrophysical parameters from the nearby vertical well is assessed. As a result, an additional input attribute is introduced to capture the uncertainty in extrapolation, while a new output attribute is incorporated as a quantitative measure of the process efficiency. Data-mining algorithms including principal components analysis (PCA) and cluster analysis are applied to improve prediction quality and model robustness by removing data correlation and by identifying internal structures among the dataset, which are novel extensions to the previous SAGD analysis study. Finally, statistical analysis is conducted to study the uncertainties in the final ANN predictions. The modeling results are demonstrated to be both reliable and acceptable. This paper demonstrates the combination of AI-based approaches and data-mining analysis can facilitate practical field data analysis, which is often prone to uncertainties, errors, biases, and noises, with high reliability and feasibility. Considering that many important system variables are typically unavailable in the public domain and, hence, are missing in the dataset, this work illustrates how practical AI approaches can be tailored to construct models capable of predicting SAGD recovery performance from only log-derived and operational variables. It also demonstrates the potential of AI models in assisting conventional SAGD analysis.

Numerical reservoir simulation is often used as a tool to forecast the production performance of oil and gas fields. Multi-phase flow functions including relative permeability and capillary pressure relationships are important components for modeling fluid distribution and movement in a porous medium, and they are strongly influenced by the pore structure. Often, relative permeability curves are habitually modified, without consideration of geomechanics effects, during the history match process. However, the depletion or injection of oil and gas reservoirs generally alters the effective stress of the system as a result of either decreasing or increasing of pore pressure. This causes changes in grain arrangement or pore structure. In this paper, a series of isotropically consolidated drained triaxial compression tests were conducted to investigate the behavior of very dense, reconstituted specimens at low effective stress conditions. After restoring the specimens to the desired reservoir conditions, the specimens were sheared under a drained compression-loading path. At various levels of axial strain, steady-state process, absolute and relative permeability tests were performed. Our results showed that in two-phase flow, the oil relative permeability was more sensitive to stress in comparison to the water relative permeability. This change of oil relative permeability was up to about 30%. Also, it was noticed from preliminary capillary pressure measurements that both cycles of drainage were significantly affected by the shear-induced contractive and dilative volume changes. This study supports the notion that relative permeability curves, instead of being kept constant, should be updated depending on the in situ stress-strain behavior.

Abstract With the increasing attention being paid to the development of unconventional reservoirs, such as shale gas or tight gas reservoirs with nanoscale pores, over the last few years, there is a great demand to develop a coherent theoretical framework that explains the transport mechanisms that take place in a nanoporous medium. In this paper, a complete modelling workflow that spans the mesoscale to the macroscale, including the lattice Boltzmann model (LBM) and Navier–Stokes equations, is introduced to reflect these transport characteristics. Gas flow for different pore diameters and Knudsen numbers is simulated by LBM. Comparison between physical experimental measurements and the LBM simulation results shows that the general transport equation is most appropriate for describing gas flow in nanoporous media and that the values of the diffusion coefficient and intrinsic permeability can be obtained simultaneously using this equation. Intrinsic permeability decreases faster than the diffusion coefficient with the decreasing average pore diameters in nanoporous media. The general transport equation has been verified to reflect the mechanisms of flow and diffusion in nanoporous media, and it also provides a theoretical basis to assess the results attained from numerical simulations.

Abstract During the hydraulic fracturing process, the fracturing fluid may cause water retention, if the nearby secondary fractures subsequently close and get disconnected due to changes in effective stress distribution during flowback and production. The circumstances and detailed mechanisms associated with this phenomenon are still poorly understood. In this work, a coupling scheme for incorporating a pressure-dependent apparent permeability model in reservoir simulation is implemented. The numerical models are subsequently used to investigate the impacts of water retention and apparent permeability modeling on gas production and water flowback. A high-resolution 3D reservoir model is constructed based on the field data obtained from the Horn River shale gas reservoir. Stochastic 3D discrete fracture netsork (DFN) model is upscaled into equivalent continuum dual-porosity dual-permeability (DPDK) model by analytical techniques. An apparent permeability (Kapp) model is employed to model transport mechanisms in nano-sized pore systems. In order to capture the pressure dependency, a novel coupling scheme is developed to facilitate the updating of Kapp and effective stress after a certain designated time interval. In addition, a novel method involving rock-type indicators is introduced to represent the open and closed states of secondary fractures, facilitating the modeling of stress-dependent closure of the secondary fracture system. Two secondary fracture closure behaviors (i.e., abrupt closure and gradual closure) are considered in our study. The results indicate that fracture closure would affect the gas production and water recovery, particularly if the near-well fractures are disconnected; the effect would be further exaggerated for a denser fracture network; fracture closure would also affect the matrix water retention near the well. Neglecting the effects of Kapp could essentially overestimate the contribution of hydraulic fracture for a certain observed gas production. The existence of secondary fractures could also enhance water loss during flowback. It is concluded that gas and water production would increase if less water is imbibed into the matrix during the shut-in period in the presence of disconnected secondary fractures. It is also observed that a shorter shut-in period may be beneficial to both water and gas recovery. This work presents a novel, yet practical, scheme for coupling stress-dependent matrix apparent permeability and fluid flow, as well as modeling pressure-dependent fracture closure. This modeling scheme can be readily integrated in most commercial reservoir simulation packages. The results have revealed several potential scenarios of water loss, along with the associated implications on optimal operational strategies and estimation of stimulated reservoir volume.

Artificial intelligence (AI) tools are used to explore the influence of shale barriers on steam-assisted gravity drainage (SAGD) production. The data are derived from synthetic SAGD reservoir simulations based on petrophysical properties and operational constraints gathered from the Suncor's Firebag project, which is representative of Athabasca oil sands reservoirs. The underlying reservoir simulation model is homogeneous and two-dimensional. Reservoir heterogeneities are modeled by superimposing sets of idealized shale barrier configurations on this homogeneous reservoir model. The individual shale barriers are categorized by their location relative to the SAGD well pair and by their geometry. SAGD production for a training set of shale barrier configurations was simulated. A network model based on AI tools was constructed to match the output of the reservoir simulation for this training set of shale barrier configurations, with a focus on the production rate and the steam-oil ratio (SOR). Then the trained AI proxy model was used to predict SAGD production profiles for arbitrary configurations of shale barriers. The predicted results were consistent with the results of the SAGD simulation model with the same shale barrier configurations. The results of this work demonstrate the capability and flexibility of the AI-based network model, and of the parametrization technique for representing the characteristics of the shale barriers, in capturing the effects of complex heterogeneities on SAGD production. It offers the significant potential of providing an indirect method for inferring the presence and distribution of heterogeneous reservoir features from SAGD field production data.

Abstract Many processes and techniques have been proposed to improve the heavy oil recovery from fractured reservoirs. Such complex processes require careful design to achieve optimal efficiency with minimal costs and environmental impacts. Steam injection is one of the options for heavy-oil recovery from fractured reservoirs but significant steam requirement for effective matrix heating due to heterogeneous structure poses important challenges in terms of cost, water availability, and environment impacts due to water processing and steam generation. Al-Bahlani and Babadagli, 2008 , Al-Bahlani and Babadagli, 2009a proposed a new process called Steam-Over-Solvent in Fractured Reservoirs (SOS-FR) by adding solvent component to minimize the heat needed. The SOS-FR technique consists of a heating phase using steam injection, subsequent solvent injection, and low temperature steam injection for solvent retrieval and additional oil recovery. Optimization of this process is a critical step to determine optimal injection (and soaking) schedules as the heterogeneous structure of this kind of reservoirs may easily yield an inefficient process due to high cost and excessive use of steam and solvent. In this study, we adopted a global optimization scheme, where genetic algorithm is integrated with orthogonal arrays and response surface proxies for better convergence behavior and higher computational efficiency, to optimize the SOS-FR process for cyclic injection option. The results show that one may be able to double the profit obtained with the benchmark model using the optimal injection scheme suggested by our optimization procedure. The results are valid for models with low fracture density; further work should be performed using models with high fracture density and that the fracture orientation is not in the direction of the injector to the producer.

Vapex (vapor extraction) is a nonthermal process that has significant potential in providing a more environmentally friendly and energy-efficient alternative to steam injection. Vaporized solvent injected in-situ dissolves into the oil and reduces oil viscosity, allowing the oil to flow to a horizontal production well via gravitational forces. While compositional simulators are available for assessing the Vapex performance, the simulation process may become difficult when taking into account the uncertainty due to reservoir heterogeneity. A semi-analytical proxy is proposed to model the process, in a way analogous to the steam-assisted gravity drainage (SAGD) model described by Butler, who demonstrated the similarity between two processes with a series of Hele-Shaw experiments and derived an analytical steady-state flow rate relationship that is comparable with the SAGD case. Solvent concentration and intrinsic diffusivity are introduced in this model instead of temperature and thermal diffusivity in SAGD. In this paper, analytical solutions and implementation details for the Vapex proxy are presented. The proposed approach is then applied to various reservoirs discretized with spatially varying rock porosity and permeability values; bitumen drainage rate and solvent penetration are calculated sequentially at grid blocks along the solvent–bitumen interface over incremental time steps. Results from this model are compared against experimental data available in the literature as well as detailed compositional simulation studies. Computational requirement of the proxy in comparison with numerical simulations is also emphasized. An important contribution from this work is that process physics are built directly into this proxy, giving it an advantage over other data-driven modeling approaches (e.g., regression). It can be used as an efficient alternative to expensive detailed flow simulations. It presents an important potential for assessing the uncertainty due to multiscale heterogeneity on effective mass transfer and the resulting recovery performance, as well as assisting decisions-making for future pilot and field development.