Extending the Smart Fields Concept to Enhanced Oil Recovery

Abstract

During the last several years Shell and its affiliates have initiated a significant number of Enhanced Oil Recovery projects covering chemical, thermal and miscible flooding applications in a variety of geological and hydrocarbon settings.Key in de-risking and sanctioning these projects is a far more detailed understanding of the fundamentals in rock and fluids physics and chemistry that have an overriding impact on the ultimate recovery and project economics. This required a significant upgrade of the experimental capability to measure relevant rock and fluid properties as well as the ability to visualize and model the EOR processes at various geological and time scales. State of the art experimental facilities have been built to enhance visualisation and understanding of flow processes in cores as well as to measure accurate physical and chemical properties.The proprietary reservoir simulator and modelling toolkit has been upgraded to include the relevant EOR processes and rock / fluid interactions in sufficient detail, covering for example In-Situ Combustion, Polymer floods, Designer Water™ flooding, Alkaline Surfactant Polymer flooding, Thermally Assisted-Gas-Oil-Gravity-Drainage, In-Situ Upgrading, a variety of Solvents and Hybrid applications at various scales, ranging from core scale to full field simulations.The Smart Fields concept pursues continuous optimisation of hydrocarbon assets, 24 hours a day, and 7 days a week. This optimisation covers locating and recovering hydrocarbons, improving performance of production (well) facilities throughout the field life cycle on timescales ranging from seconds to field life. An important part of the Smart Fields concept is Closed Loop Reservoir Management (CLRM), which ensures that data gathered in the operations phase is used to improve quality of reservoir models and allow a faster field management cycle. Novel robust mathematical optimisation algorithms and control methods are rapidly maturing to assist automatic history matching, high-grading geological reservoir model ensembles and reducing the uncertainties. The desired outcome is better well offtake or injection policies that are also robust against remaining key uncertainties.Extending the Smart Field concepts to EOR requires the definition of the appropriate levels of smartness for EOR projects for each element of the Smart Field Life Cycle, which consists of: data acquisition, modelling, integrated decision making and operational field management, each with a high level of integration and automation.

Architecture and Implementation of an Optimization Decision Support System

“…Economical Value -too theoretical Perhaps a more correct, but also more theoretical approach would be to optimize on asset economical value [8]. This would take into account a slightly simplified version of the short-term financial approach, and augment it by placing the financial returns and expected returns in the context of the long term, and comparing the time value of money in real terms by applying a Nett Present Value of expected cash flows.…”

“…A combination of the above applications is required to successfully and optimally manage an EOR asset [8].…”

Successful Real-Time Optimization of a Highly Complex, Integrated Gas System: Intelligent Energy in the Real World

“…Perhaps a more correct, but also more theoretical approach would be to optimize on asset economical value [8]. This would take into account a slightly simplified version of the short-term financial approach, and augment it by placing the financial returns and expected returns in the context of the long term, and comparing the time value of money in real terms by applying a Nett Present Value of expected cash flow and assets.…”

“…A combination of the above applications is required to successfully and optimally manage an EOR asset [8]. A combination of the above applications is required to successfully and optimally manage an EOR asset [8].…”

Closed-loop field development with multipoint geostatistics and statistical performance assessment

“…Most papers on CLRM investigated the application of particular model calibration and optimization approaches for water-flooding operations (e.g., Aitokhuehi and Durlofsky (2005); Chen et al (2009)). CLRM has also been applied to SAGD operations (Regtien, 2010), and for the management of geological carbon storage operations (Cameron and Durlofsky, 2014). It's worth mentioning that Jansen et al (2005Jansen et al ( , 2009 envisioned the use of optimal position of sidetracks or infill wells in closed-loop optimization, which is now performed in CLFD.…”