Since decades, steam-assisted oil recovery processes have been successfully deployed in heavy oil reservoirs to extract bitumen/heavy oil. Current resource allocation practices mostly involve reservoir model-based open loop optimization at the planning stage and its periodic recurrence. However, such decades-old strategies need a complete overhaul as they ignore dynamic changes in reservoir conditions and surface facilities, ultimately rendering heavy oil production economically unsustainable in the low-oil-price environment. Since steam supply costs account for more than 50% of total operating costs, a data-driven strategy that transforms the data available from various sensors into meaningful steam allocation decisions requires further attention.
In this research, we propose a purely data-driven algorithm that maximizes the economic objective function by allocating an optimal amount of steam to different well pads. The method primarily constitutes two components: forecasting and nonlinear optimization. A dynamic model is used to relate different variables in historical field data that were measured at regular time intervals and can be used to compute economic performance indicators (EPI). The variables in the model are cumulative in nature since they can represent the temporal changes in reservoir conditions. Accurate prediction of EPI is ensured by retraining regression model using the latest available data. Then, predicted EPI is optimized using a nonlinear optimization algorithm subject to amplitude and rate saturation constraints on decision variables i.e., amount of steam allocated to each well pad.
Proposed steam allocation strategy is tested on 2 well pads (each containing 10 wells) of an oil sands reservoir located near Fort McMurray in Alberta, Canada. After exploratory analysis of production history, an output error (OE) model is built between logarithmically transformed cumulative steam injection and cumulative oil production for each well pad. Commonly used net-present-value (NPV) is considered as EPI to be maximized. Optimization of the objective function is subject to distinct operating conditions and realistic constraints. By comparing results with field production history, it can be observed that optimum steam injection profiles for both well pads are significantly different than that of a field. In fact, the proposed algorithm provides smooth and consistent steam injection rates, unlike field injection history. Also, the lower steam-oil ratio is achieved for both well pads, ultimately translating into ~19 % higher NPV when compared with field data.
Inspired from state-of-the-art control techniques, proposed steam allocation algorithm provides a generic data-driven framework that can consider any number of well pads, EPIs, and amount of past data. It is computationally inexpensive as no numerical simulations are required. Overall, it can potentially reduce the energy required to extract heavy oil and increase the revenue while inflicting no additional capital cost and reducing greenhouse gas emissions.
Steam-assisted gravity drainage (SAGD) is a thermal-recovery process to produce bitumen from oil sands. In this technology, steam injected in the reservoir creates a constantly evolving steam chamber while heated bitumen drains to a production well. Understanding the geometry and the rate of growth of the steam chamber is necessary to manage an economically successful SAGD project. This work introduces an approximate physics-discrete simulator (APDS) to model the steam-chamber evolution. The algorithm is formulated and implemented using graph theory, simplified porous-media flow equations, heat-transfer concepts, and ideas from discrete simulation. The APDS predicts the steam-chamber evolution in heterogeneous reservoirs and is computationally efficient enough to be applied over multiple geostatistical realizations to support decisions in the presence of geological uncertainty. The APDS is expected to be useful for selecting well-pair locations and operational strategies, 4D-seismic integration in SAGD-reservoir characterization, and caprock-integrity assessment.
Field studies have shown that, if an inclined fracture has a significant inclination angle from the vertical direction or the fracture has a poor growth along the inclined direction, this fracture probably cannot fully penetrate the formation, resulting in a partially penetrating inclined fracture (PPIF) in these formations. It is necessary for the petroleum industry to conduct a pressure-transient analysis on such fractures to properly understand the major mechanisms governing the oil production from them. In this work, we develop a semianalytical model to characterize the pressure-transient behavior of a finite-conductivity PPIF. We discretize the fracture into small panels, and each of these panels is treated as a plane source. The fluid flow in the fracture system is numerically characterized with a finite-difference method, whereas the fluid flow in the matrix system is analytically characterized on the basis of the Green’s-function method. As such, a semianalytical model for characterizing the transient-flow behavior of a PPIF can be readily constructed by coupling the transient flow in the fracture and that in the matrix. With the aid of the proposed model, we conduct a detailed study on the transient-flow behavior of the PPIFs. Our calculation results show that a PPIF with a finite conductivity in a bounded reservoir can exhibit the following flow regimes: wellbore afterflow, fracture radial flow, bilinear flow, inclined-formation linear flow, vertical elliptical flow, vertical pseudoradial flow, inclined pseudoradial flow, horizontal-formation linear flow, horizontal elliptical flow, horizontal pseudoradial flow, and boundary-dominated flow. A negative-slope period can appear on the pressure-derivative curve, which is attributed to a converging flow near the wellbore. Even with a small dimensionless fracture conductivity, a PPIF can exhibit a horizontal-formation linear flow. In addition to PPIFs, the proposed model also can be used to simulate the pressure-transient behavior of fully penetrating vertical fractures (FPVFs), partially penetrating vertical fractures (PPVFs), fully penetrating inclined fractures (FPIFs), and horizontal fractures (HFs).
Shale heterogeneities often impede the development of steam chamber in many steam-assisted gravity drainage (SAGD) projects. Unfortunately, static data alone is generally insufficient for inferring the corresponding distribution of shale barriers. This study presents a novel data-driven modeling workflow, which integrates deep learning (DL) and data analytics techniques to analyze production profiles from horizontal well pairs and temperature profiles from vertical observation wells, for the inference of shale barrier characteristics.
Field data gathered from several Athabasca oil sands projects are extracted to build a set of synthetic SAGD models, where the geometries, proportions and spatial distribution of shale barriers are modeled stochastically. Numerical flow simulation is performed on each realization; the corresponding production/injection time-series data, as well as temperature profiles from one vertical observation well, are recorded. A large dataset is assembled for the development of data-driven models: wavelet analysis and other data analysis techniques are performed to extract relevant input features from the temperature and production profiles; a novel parameterization scheme is also proposed to formulate the output variables that would effectively describe the detailed distribution of shale barriers. DL, such as convolutional neural network, together with other data analytics techniques are applied to capture the complex and nonlinear relationships between these input and output variables.
The feasibility of the developed workflow is validated using synthetic test cases. Salient features capturing the impacts of shale barriers are extracted. It is observed from the production time-series data that, as the steam chamber approaches a shale barrier, a decline pattern is noticeable until the steam chamber advances around the shale barrier. An obstruction in the steam chamber development can also be noted in the temperature profiles, as steam is trapped by shale barriers that are located reasonably close to the horizontal well pair. This observation is confirmed by comparing the petrophysical logs and the temperature profiles at the observation wells. Analyzing both temperature and production data could help to infer the size of shale barriers in the inter-well regions. Finally, the model outputs are used to generate an ensemble of heterogeneous SAGD realizations that correspond to the input production and temperature time-series data.
This study offers a complementary and computationally-efficient tool for inference of stochastically-distributed shale barriers in SAGD models, which can be subjected to detailed history-matching workflows. It is the first time that data-driven models are used to analyze both production data from horizontal production well pairs and temperature profiles from a vertical observation well for inferring SAGD reservoir heterogeneities. The results illustrate the potential for application of data analytics in reservoir modeling and flow simulation analysis. The developed workflow also can be extended to characterize reservoir heterogeneities in other recovery processes.
Huang, Hai (Xi'an Shiyou University, Shaanxi Key Laboratory of Advanced Stimulation Technology for Oil & Gas Reservoirs) | Babadagli, Tayfun (University of Alberta) | Chen, Xin (University of Alberta) | Li, Huazhou (University of Alberta)
Tight sands are abundant in nanopores leading to a high capillary pressure and normally a low fluid injectivity. As such, spontaneous imbibition might be an effective mechanism for improving oil recovery from tight sands after fracturing. The chemical agents added to the injected water can alter the interfacial properties, which could help further enhance the oil recovery by spontaneous imbibition. This study explores the possibility of using novel chemicals to enhance oil recovery from tight sands via spontaneous imbibition. We experimentally examine the effects of more than ten different chemical agents on spontaneous imbibition, including a cationic surfactant (C12TAB), two anionic surfactants (O242 and O342), an ionic liquid (BMMIM BF4), a high pH solution (NaBO2), and a series of house-made deep eutectic solvents (DES3-7, 9, 11 and 14). Experimental results indicate that the ionic liquid and cationic surfactant used in this study are detrimental to spontaneous imbibition and decrease the oil recovery from tight sands. The high pH NaBO2 solution does not demonstrate significant effect on improving oil recovery, even though it significantly reduces oil-water interfacial tension (IFT). The anionic surfactants (O242 and O342) are effective in enhancing oil recovery from tight sands through oil-water IFT reduction and emulsification effects. The DESs drive the rock surface to be more water-wet and a specific formulation (DES9) leads to much improvement on oil recovery under counter-current imbibition condition. This preliminary study would provide some knowledge about how to optimize the selection of chemicals for improving oil recovery from tight reservoirs.
In comparison to Steam-Assisted Gravity-Drainage (SAGD), the technique of injecting of warm solvent vapor into the formation for heavy oil production offers many advantages, including lower capital and operational costs, reduced water usage, and less greenhouse gas emission. However, to select the optimal operational parameters for this process in heterogeneous reservoirs is non-trivial, as it involves the optimization of multiple distinct objectives including oil production, solvent recovery (efficiency), and solvent-oil ratio. Traditional optimization approaches that aggregate numerous competing objectives into a single weighted objective would often fail to identify the optimal solutions when several objectives are conflicting. This work aims to develop a hybrid optimization framework involving Pareto-based multiple objective optimization (MOO) techniques for the design of warm solvent injection (WSI) operations in heterogeneous reservoirs.
First, a set of synthetic WSI models are constructed based on field data gathered from several typical Athabasca oil sands reservoirs. Dynamic gridding technique is employed to balance the modeling accuracy and simulation time. Effects of reservoir heterogeneities introduced by shale barriers on solvent efficiency are systematically investigated. Next, a state-of-the-art MOO technique, non-dominated sorting genetic algorithm II, is employed to optimize several operational parameters, such as bottomhole pressures, based on multiple design objectives. In order to reduce the computational cost associated with a large number of numerical flow simulations and to improve the overall convergence speed, several proxy models (e.g., response surface methodology and artificial neural network) are integrated into the optimization workflow to evaluate the objective functions.
The study demonstrates the potential impacts of reservoir heterogeneities on the WSI process. Models with different heterogeneity settings are examined. The results reveal that the impacts of shale barriers may be more/less evident under different circumstances. The proxy models can be successfully constructed using a small number of simulations. The implementation of proxy models significantly reduces the modeling time and storages required during the optimization process. The developed workflow is capable of identifying a set of Pareto-optimal operational parameters over a wide range of reservoir and production conditions.
This study offers a computationally-efficient workflow for determining a set of optimum operational parameters relevant to warm solvent injection process. It takes into account the tradeoffs and interactions between multiple competing objectives. Compared with other conventional optimization strategies, the proposed workflow requires fewer costly simulations and facilitates the optimization of multiple objectives simultaneously. The proposed hybrid framework can be extended to optimize operating conditions for other recovery processes.
Cold heavy oil production with sand (CHOPS) is a non-thermal primary process that is widely adopted in many weakly consolidated heavy oil deposits around the world. However, only 5 to 15% of the initial oil in place is typically recovered. Several solvent-assisted schemes are proposed as follow-up strategies to increase the recovery factor in post-CHOPS operations. The development of complex, heterogeneous, high-permeability channels or wormholes during CHOPS renders the analysis and scalability of these processes challenging. One of the key issues is how to properly estimate the dynamic growth of wormholes during CHOPS. Existing growth models generally offer a simplified representation of the wormhole network, which, in many cases, is denoted as an extended wellbore. Despite it is commonly acknowledged that wormhole growth due to sand failure is likely to follow fractal statistics, there are no established workflows to incorporate geomechanical constraints into the construction of these fractal wormhole patterns.
A novel dynamic wormhole growth model is developed to generate a set of realistic fractal wormhole networks during the CHOPS operations. It offers an improvement to the Diffusion Limited Aggregation (DLA) algorithm with a sand-arch-stability criterion. The outcome is a fractal pattern that mimics a realistic wormhole growth path, with sand failure and fluidization being controlled by geomechanical constraints. The fractal pattern is updated dynamically by coupling compositional flow simulation on a locally-refined grid and a stability criterion for the sand arch: the wormhole would continue expanding following the fractal pattern, provided that the pressure gradient at the tip exceeds the limit corresponding to a sand-arch-stability criterion. Important transport mechanisms including foamy oil (non-equilibrium dissolution of gas) and sand failure are integrated.
Public field data for several CHOPS fields in Canada is used to examine the results of the dynamic wormhole growth model and flow simulations. For example, sand production history is used to estimate a practical range for the critical pressure gradient representative of the sand-arch-stability criterion. The oil and sand production histories show good agreement with the modeling results.
In many CHOPS or post-CHOPS modeling studies, constant wormhole intensity is commonly assigned uniformly throughout the entire domain; as a result, the ensuing models are unlikely to capture the complex heterogeneous distribution of wormholes encountered in realistic reservoir settings. This work, however, proposes a novel model to integrate a set of statistical fractal patterns with realistic geomechanical constraints. The entire workflow has been readily integrated with commercial reservoir simulators, enabling it to be incorporated in practical field-scale operations design.
The water recovered from hydraulic-fracturing operations (i.e., flowback water) is highly saline, and can be analyzed for reservoir characterization. Past studies measured ion-concentration data during imbibition experiments to explain the production of saline flowback water. However, the reported laboratory data of ion concentration are approximately three orders of magnitude lower than those reported in the field. It has been hypothesized that the significant surface area created by hydraulic-fracturing operations is one of the primary reasons for the highly saline flowback water.
In this study, we investigate shale/water interactions by measuring the mass of total ion produced (TIP) during water-imbibition experiments. We conduct two sets of imbibition experiments at low-temperature/low-pressure (LT/LP) and high-temperature and high-pressure (HT/HP) conditions. We study the effects of rock surface area (As), temperature, and pressure on TIP during imbibition experiments. Laboratory results indicate that pressure does not have a significant effect on TIP, whereas increasing As and temperature both increase TIP. We use the flowback-chemical data and the laboratory data of ion concentration to estimate the fracture surface area (Af) for two wells completed in the Horn River Basin (HRB), Canada. For both wells, the estimated Af values from LT/LP and HT/HP test results have similar orders of magnitude (approximately 5.0×106 m2) compared with those calculated from production and flowback rate-transient analysis (RTA) (approximately 106 m2). The proposed scaleup procedure can be used as an alternative approach for a quick estimation of Af using early-flowback chemical data.
A rupture of buckled steel pipes on the tensile side of a cross-section is studied in this paper as the most plausible case of ultimate failure for the pressurized buried pipelines under monotonically increasing curvature. Finite element simulation of full-scale bending tests on two pressurized X80 pipes with different yield-to-tensile strength (Y/T) ratios were conducted. The Y/T ratio and internal pressure were identified as the crucial factors that have a coupled effect on the ultimate failure mode of buckled pipes. That is, the high values of Y/T ratio and internal pressure mutually trigger the rupture of buckled pipes on the opposite side of the wrinkling.
Steel pipelines are so ductile and can accommodate a large amount of post-buckling deformations while preserving their operational safety and structural integrity. To benefit from this outstanding quality and prevent the buckled (wrinkled) pipelines from premature rupture, the postbuckling behavior of the steel pipes should be well understood.
Rupture is one of the major failure limits to the integrity of pipelines that endangers the environment as well as the public safety and property. Comprehensive experimental and numerical studies on the fracture of buckled steel pipes (Das, 2003; Sen, 2006; Mohajer Rahbari, 2017) show that under increased monotonic curvature, successive buckles (wrinkling) are formed on the compressive side of the wall, and the occurrence of rupture at the wrinkling location is unlikely because of the ductile nature of steel material. Rupture of wrinkling can occur once buried pipelines are subject to a very rare and changing boundary conditions accompanied by extremely large plastic deformations toward tearing the wrinkled wall (Ahmed, 2011). However, experiments have shown that the increasing curvature can easily trigger the postbuckling rupture of the tensile wall on the opposite side of the wrinkling (Sen, 2006; Mitsuya et al., 2008; Tajika and Suzuki, 2009; Igi et al., 2011; Tajika et al., 2011; Mitsuya and Motohashi, 2013; Mitsuya and Sakanoue, 2015). This mode of failure seems very likely to be the rupture limit of the wrinkled pipes, as it occurs following the same regime of monotonic bending deformations that have previously made the pipe buckle.
Steam-assisted gravity drainage (SAGD) is a thermal-recovery process to produce bitumen from deep oil-sands deposits. The efficiency of the SAGD operation depends on developing a uniform steam chamber and maintaining an optimal subcool (difference in saturation and actual temperature) along the length of the horizontal well pair. Heterogeneity in reservoir properties might lead to suboptimal subcool levels without the application of closed-loop control. Recently, model-predictive control (MPC) has been proposed for real-time feedback control of SAGD well pairs based on real-time production, temperature, and pressure data along with other well and surface constraint information; however, reservoir dynamics has been represented using extremely simplified and unrealistic models. Because SAGD is a complex, spatially distributed, nonlinear process, an MPC framework with models that account for nonlinearity over an extended control period is required to achieve optimized subcool and steam conformance.
In this research, two novel work flows are proposed to handle nonlinear reservoir dynamics in MPC. The first approach is adaptive MPC, and includes continuous re-estimation of the model at each control interval. This allows the evolution of the coefficients of a fixed-model structure such that the updated system-identification model in the MPC controller reflects current reservoir dynamics adequately. Another approach, gain-scheduled MPC, decomposes the subcool-control problem in a parallel manner, and uses a bank of multiple controllers rather than only one controller. This ensures effective control of the nonlinear reservoir system even in adverse control situations by using appropriate variations in input parameters based on the operating region.
The work flows are implemented using a history-matched numerical model of a reservoir in northern Alberta. Steam-injection rates and liquid-production rate are considered input variables in MPC, constrained to available surface facilities. The well pair is divided into multiple sections, and the subcool of each section is considered an output variable. Results are compared with actual field data (in which no control algorithm is used), and are analyzed on the basis of two criteria: (1) Do all subcools track the set point while maintaining stability in input variables? and (2) Does the net present value (NPV) of oil improve with adaptive and gain-scheduled MPC? In general, we conclude that both adaptive and gain-scheduled MPC provide superior tracking of subcool set points and, hence, better steam conformance caused by adequate representation of reservoir dynamics by re-estimation of coefficients and multiple controllers, respectively. In addition, the results indicate stability in input parameters and improvement in economic performance. NPV is improved by 23.69 and 10.36% in case of adaptive and gain-scheduled MPC, respectively.
The proposed work flows can improve the NPV of an SAGD reservoir by optimizing the well-operational parameters while considering constraints of surface facilities and minimizing environmental footprint.