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.
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.
This paper discusses the application of IIoT in various areas of oil and gas upstream. It elaborates on the drivers of IIoT, presents the advantages and benefits and describes the challenges faced as of today in the implementation. IIoT and cloud computing work hand in hand. IIoT generates huge amount of data and cloud computing provides a pathway to present this data is a useful way and travel to the end user. A detail evaluation of the investment in using this technology and its anticipated returns are demonstrated. IIoT is believed to be an emerging solution for oil and gas complexities. The main drivers behind this technology are data storage, data analytics, reliability improvement and materiality assessment and control. The application of IIoT in areas of artificial lift optimization, Supply chain in real time, cyclic steam stimulation and flow assurance is described. This technology provides real time solution for dynacards interpretation and analysis for Sucker rod pumps, operating point analysis for Electrical submersible pumps and predicted cumulative production for all artificial lift optimization; efficient planning and waste elimination for supply chain and logistics; real time steam quality and quantity check for CSS and a complete digital approach to reservoir management and flow assurance. The main benefits of this technology are reduced MTBF, high efficiency, improved HSE standards, Instantaneous control over production loss, collaborative decisions leading to fast turnaround, highly responsive supply chain and enhancing environmental footprint. This has helped substantially in real time management of wells by exception and alerts in form of intelligent alarms indicating any deviation in the expected behaviour. This has significantly brought down the non-productive time (NPT). However, this paradigm shift comes with a substantial cost. The technical challenges include the data security, protocol non-uniformity, possible data loss and limitations of redundant system.
Xiong, Hao (University of Oklahoma) | Huang, Shijun (China University of Petroleum, Beijing) | Devegowda, Deepak (University of Oklahoma) | Liu, Hao (China University of Petroleum, Beijing) | Li, Hao (University of Oklahoma) | Padgett, Zack (Univiersity of Oklahoma)
Hao Xiong, University of Oklahoma; Shijun Huang, China University of Petroleum, Beijing; Deepak Devegowda, University of Oklahoma; Hao Liu, China University of Petroleum, Beijing; and Hao Li and Zack Padgett, University of Oklahoma Summary Steam-assisted gravity drainage (SAGD) is the most-effective thermal recovery method to exploit oil sand. The driving force of gravity is generally acknowledged as the most-significant driving mechanism in the SAGD process. However, an increasing number of field cases have shown that pressure difference might play an important role in the process. The objective of this paper is to simulate the effects of injector/producer-pressure difference on steam-chamber evolution and SAGD production performance. A series of 2D numerical simulations was conducted using the MacKay River and Dover reservoirs in western Canada to investigate the influence of pressure difference on SAGD recovery. Meanwhile, the effects of pressure difference on oil-production rate, stable production time, and steam-chamber development were studied in detail. Moreover, by combining Darcy's law and heat conduction along with a mass balance in the reservoir, a modified mathematical model considering the effects of pressure difference is established to predict the SAGD production performance. Finally, the proposed model is validated by comparing calculated cumulative oil production and oil-production rate with the results from numerical and experimental simulations. The results indicate that the oil production first increases rapidly and then slows down when a certain pressure difference is reached. However, at the expansion stage, lower pressure difference can achieve the same effect as high pressure difference. In addition, it is shown that the steam-chamber-expansion angle is a function of pressure difference. Using this finding, a new mathematical model is established considering the modification of the expansion angle, which (Butler 1991) treated as a constant. With the proposed model, production performance such as cumulative oil production and oil-production rate can be predicted. The steam-chamber shape is redefined at the rising stage, changing from a fanlike shape to a hexagonal shape, but not the single fanlike shape defined by (Butler 1991). This shape redefinition can clearly explain why the greatest oil-production rate does not occur when the steam chamber reaches the caprock.
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.
Hadavand, Mostafa (University of Alberta) | Carmichael, Paul (ConocoPhillips Canada) | Dalir, Ali (ConocoPhillips Canada) | Rodriguez, Maximo (ConocoPhillips Canada) | Silva, Diogo F. S. (University of Alberta) | Deutsch, Clayton Vernon (University of Alberta)
Mostafa Hadavand, University of Alberta; Paul Carmichael, Ali Dalir, and Maximo Rodriguez, ConocoPhillips Canada; and Diogo F. S. Silva and Clayton V. Deutsch, University of Alberta Summary 4D seismic is one of the main sources of dynamic data for heavy-oil-reservoir monitoring and management. Thus, the large-scale nature of fluid flow within the reservoir can be evaluated through information provided by 4D-seismic data. Such information may be described as anomalies in fluid flow that can be inferred from the unusual patterns in variations of a seismic attribute. During steam-assisted gravity drainage (SAGD), the steam-chamber propagation is fairly clear from 4D-seismic data mainly because of changes in reservoir conditions caused by steam injection and bitumen production. Anomalies in the propagation of the steam chamber reflect the quality of fluid flow within the reservoir. A practical methodology is implemented for integration of 4D seismic into SAGD reservoir characterization for the Surmont project. Introduction One of the main objectives in petroleum-reservoir modeling is to predict the future performance of the reservoir under a recovery process. It is not possible to establish the true spatial distribution of reservoir properties using limited data. Thus, the modeling process is ill-posed and subject to uncertainty (Pyrcz and Deutsch 2014). Geostatistical simulation provides a framework to quantify geological uncertainty that is represented by multiple equally probable realizations of the reservoir model. The uncertainty can be reduced by integration of all available sources of data, including static and dynamic (time-variant) data. However, each source of data provides information at different scales and levels of precision. Although there are well-established geostatistical techniques to generate stochastic realizations of the reservoir conditioned to static data, such as local measurements from wells and 2D/3D-seismic data, effective integration of dynamic data remains a major challenge. Time-lapse seismic, or 4D seismic, is one of the main dynamic sources of data for heavy-oil-reservoir monitoring and management. It contains valuable information regarding fluid movement, temperature, pressure buildup, and quality of fluid flow within the reservoir during a recovery process (Lumley and Behrens 1998; Gosselin et al. 2001). For SAGD, the evolution of the steam chamber over time is fairly clear in 4D-seismic images.
Kumar, Anjani (Computer Modelling Group Ltd) | Novlesky, Alex (Computer Modelling Group Ltd) | Bityutsky, Erykah (Computer Modelling Group Ltd) | Koci, Paul (Consultant for Occidental Petroleum Corporation) | Wightman, Jeff (Occidental Petroleum Corporation)
Heavy oil reservoirs often require thermal enhanced oil recovery (EOR) processes to improve the mobility of the highly viscous oil. When working with steam flooding operations, finding the optimal steam injection rates is very important given the high cost of steam generation and the current low oil price environment. Steam injection and allocation then becomes an exercise of optimizing cost, improving productivity and net present value (NPV). As the field matures, producers are faced with declining oil rates and increasing steam oil ratios (SOR). Operators must work to reduce injection rates on declining groups of wells to maintain a low SOR and free up capacity for newer, more productive groups of wells. Operators also need a strong surveillance program to monitor field operational parameters like SOR, remaining Oil-in-Place (OIP) distribution in the reservoir, steam breakthrough in the producers, temperature surveys in observation wells etc. Using the surveillance data in conjunction with reservoir simulation, operators must determine a go-forward operating strategy for the steam injection process.
The proposed steam flood optimization workflow incorporates field surveillance data and numerical simulation, driven by machine learning and AI enabled Algorithms, to predict future steam flood reservoir performance and maximize NPV for the reservoir. The process intelligently determines an optimal current field level and well level injection rates, how long to inject at that rate, how fast to reduce rates on mature wells so that it can be reallocated to newly developed regions of the field. A case study has been performed on a subsection of a Middle Eastern reservoir containing eight vertical injectors and four sets of horizontal producers with laterals landed in multiple reservoir zones. Following just the steam reallocation optimization process, NPV for the section improved by 42.4% with corresponding decrease in cumulative SOR by 24%. However, if workover and alternate wellbore design is considered in the optimization process, the NPV for the section has the potential to be improved by 94.7% with a corresponding decrease in cumulative SOR by 32%. This workflow can be extended and applied to a full field steam injection project.
Heavy oil production poses multiple challenges on oil companies such as intricate operations (complexity), high operational costs (efficiency control), and solutions for high viscosity and low API fluids (specific technology). This paper describes the technical workflows implemented in a heavy oil field located in the Middle Magdalena Valley basin in Colombia to assist in the complexity of an operation where cyclic steam injection is applied. The complexity of producing heavy oil is due to its high viscosity at repository conditions, which limits the mobility of the fluid and the draining effect to a relative small area around the wellbore, thus it is required an infrastructure that involves many wells, flowlines, manifolds, and facilities to produce this type of crude oil. To support the surveillance and the technical decision-making processes in an operation with more than 300 wells, effective workflows were designed and implemented with the aims to eliminate mistaken decisions, optimize resources, and contribute to cost optimization. The algorithms, foundation of these workflows, are presented with the analysis of different elements taken into consideration during the technical design process, such as well intervention program, field infrastructure, daily reports, and current well parameters. Additionally, four specific targets are discussed: Heat injection surveillance, which follows program plan vs current execution, including the calculation of heat values from operational conditions (pressure, steam quality, mass rate, and running time); Well test schedule, which organizes the wells test plan in a hierarchy considering flowline connections, well services plan, and priority well list (time since last test, wells with abnormal behavior); Dinalog plan, which is the foundation for artificial lift control; Steam injection scheme, which creates a suggested ranking of wells requiring steam stimulation based on current conditions (Wcut, WHT, production rate), time since last stimulation, and steam/oil ratio. The developed workflows are applicable in both heavy oil fields, and light oil fields with a large number of wells, and they can be a valuable foundation for digital oil fields providing support for technical management and contributing to resolve the challenges of a vast operation.
Zeidan, A. A (Abdulaziz Erhamah Kuwait Oil Company) | Redha, Reeham Ali (Abdulaziz Erhamah Kuwait Oil Company) | Williams, Darryl D. (Shell Company in Kuwait) | Montero, Jacobo Enrique (Shell Company in Kuwait)
Kuwait Oil Company (KOC) is operating two Heavy Oil fields. Field A aims at production by Cyclic Steam Stimulation (CSS), followed by steam flood. Field B envisages primary recovery through cold production, followed by non-thermal Enhanced Oil Recovery (EOR). This requires drilling and completion of large number of wells. Implementing Well, Reservoir and Facilities Management (WRFM) and Smart Field approach will be a key requirement for operation excellence in these fields.
Currently both fields have some wells in production, mostly as single isolated wells or wells in 5-acre/ 10-acre spacing. These pilot projects aimed at de-risking the commercial phase, which is to follow in the coming years. These wells are the training ground for young KOC staff to learn how to work in integrated teams using WRFM processes.
WRFM processes are tailor-made for KOC's operating environment. These processes include Digital Oil Field based on Exception Based Surveillance (to flag out only those wells and facilities outside of their operating envelope and/or optimization window) and Production System Optimization. This would help to eliminate operational bottlenecks, leading to optimization in manpower to deal with large number of wells. It is expected to be achieved by combining existing best practices of International Oil Companies (IOC) with existing KOC applications, leveraging successful global practices.
The paper shall highlight the timeline, activities and organizational changes underway to effect the transformation from existing operation to a larger and more complex development that includes continuous drilling, completion and well intervention (CWI) and facilities installation occurring simultaneously.
The implementation of WRFM Processes along with Digital field will achieve the production and operation goals by reducing well, artificial lift, and facility downtime. This innovative production optimization system by enabling efficient decision-making process shall lower the cost per bbl. and reduce down time by implementing automated surveillance workflow.