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.
The addition of a hydrocarbon condensate to steam operations in heavy-oil and bitumen reservoirs has emerged as a potential technology to improve not only oil recovery but also energy efficiency. Thermal steam stimulation is considered the most effective of current methods for heavy-oil production. However, the method has problems with low coverage by steam injection and decreased efficiency.
This article highlights interesting applications of machine learning in the oil and gas industry in drilling, formation evaluation, and reservoir engineering. Each project uses a data-driven model to solve a previously complex problem using machine learning to augment an existing solution. Graham Hack shares his experience working for a large and a small E&P company and discusses how the role of young professionals can differ in these companies. This article presents three important R&D realms that demonstrate good collaborative efforts between independents and academic research institutions. Three women working in R&D offer perspectives on their work, why they joined that branch of the industry, what projects they work on, and what keeps them challenged.
An early commitment to integrate MPD into an HP/HT drilling operation can make MPD more than just an enabling tool and turn it into a performance tool that offers significant operational benefits. The optimization model presented in the complete paper is the first multioperator offshore network-optimization model that considers decommissioning in the Netherlands. A study was required to determine the origin of the tremor, evaluate if it could be followed by other tremors in the future, and estimate its magnitude. A reservoir-monitoring system has been installed on a medium-heavy-oil onshore field in the context of redevelopment by gravity-assisted steamflood.
A horizontal-steam-injection pilot project has been under way for the last 4 years in the Kern River heavy-oil field in the southern San Joaquin Valley of California. This paper presents an investigation into the effect of catalytic nanoparticles on the efficiency of recovery from continuous steam injection. The operator has initiated a cyclic-steam-stimulation project in the Opal A diatomite of the Sisquoc formation on the Careaga lease in the Orcutt oil field in Santa Barbara County, California.
This paper studies the technical and economic viability of this EOR technique in Eagle Ford shale reservoirs using natural gas injection, generally after some period of primary depletion, typically through long, hydraulically fractured horizontal-reach wells. The Eagle Ford formation has produced approximately 2 billion bbl of oil during the last 7 years, yet its potential may be even greater. Using improved oil-recovery (IOR) methods could result in billions of additional barrels of production.
This paper uses a simulation model to evaluate and compare the thermal efficiency of five different completion design cases during the SAGD circulation phase in the Lloydminster formation in the Lindbergh area in Alberta, Canada. The cost reduction per barrel of oil produced and the extension of sustainable production life by optimization have been two major areas of focus, but the investments in new technologies and recovery-improvement research have not received sufficient attention during the downturn. This paper covers the staged field-development methodology, including analysis and evaluation of various development concepts, that enabled the company to optimize both completion design and artificial-lift selection, reducing downtime and lowering operating costs by nearly 50%. This paper shares experience gained in the Ashalchinskoye heavy-oil field with a two-wellhead SAGD modification. As a result of a pilot for this technology in Russia, the accumulated production of three pairs of these wells is greater than 200,000 tons.
This paper uses a simulation model to evaluate and compare the thermal efficiency of five different completion design cases during the SAGD circulation phase in the Lloydminster formation in the Lindbergh area in Alberta, Canada. The authors discuss a new way of extracting deformation information from radar imagery, contributing to improved accuracy of InSAR surface-elevation monitoring. A Canadian research organization believes the country’s oilfield technology could help another energy sector drive down its costs and it may work out for heavy oil producers too.