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 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.