At Total, integrating our areas of expertise also means integrating the tools we use to model every aspect of our developments, from the reservoir through to the processing facilities. Our proprietary innovations as well as others developed with industrial partners give us integrated solutions tailored to our needs for every phase of a field’s lifetime. At stake is the optimization of both economic and technological performance.
Leveraging Tools to Create Value
From extraction from the subsurface to export from production hubs, petroleum fluids traverse a broad range of environments: reservoirs, wells, surface networks and finally processing facilities. To simulate fluid flows through each of these highly specific environments, reservoir engineers, well engineers, flow assurance engineers and process engineers all use the dedicated tools of their trade. Such tools need to be specialized in order to “capture” the complexity of the physical-chemical phenomena involved at each step of the production chain. However, the key to optimizing performance lies in decompartmentalizing our various areas of expertise and supporting our multidisciplinary approach with integrated modeling tools.
Because it gives us the capacity to simulate the entire development scheme – from reservoirs to surface facilities – as a dynamic system, integrated asset modeling allows us to maximize value creation from the field as a whole and from its development, until the end of the field’s producing life.
The benefits of integrated asset modeling are not confined to economics; there are significant technical advantages as well. Integration puts an end to data imports and exports between the simulators of each discipline, saving time and eliminating the data losses inherent to such transfers. Thus, even while shortening the time we spend on field planning, integrated modeling also makes our studies more robust. For example, it ensures consistency between the assumptions used by the reservoir engineer and the process engineer, and allows for the integration of uncertainties – especially the impact of subsurface uncertainties on the design of surface facilities.
Integration Approaches Tailored to Each Objective
We have developed integrated modeling strategies consistent with the “fit for purpose” approach that prevails across the entire exploration and production process. Accordingly, we have three different stages of integration, and the complexity of each one is adjusted to the purpose of each type of study.
1. Exploration phase – At this stage, it is appropriate to simplify downhole/surface simulations to ensure quick delivery of the results needed for decision-making.
2. Development phase – Our integrated models become steadily more complex as the process advances from the conceptual studies used to screen different concepts through to the choice of the most effective development design, defined during the pre-FEED phase. This approach revolves around a number of explicit options for coupling the different simulators (reservoir, well and surface networks, and surface processes).
Total can rely on two major resources that facilitate our deployment of these innovative solutions:
- Our partnerships with software vendors, which place us at the nerve center of development of new functionalities to enrich the options we have at our disposal. In the past two years, for example, we helped introduce a coupling that allows for direct integration of the dynamic simulations of wells and surface networks during a steady state flow simulation, as well as the coupling of a surface network simulator and a process simulator;
- The computational power of our Pangea high-performance computer, which makes Total the only oil major able to run integrated simulations with a high level of detail on 500 to 1,000 processors. This is indispensable if we are to carry out complex simulations within acceptable computing times.
3. Production phase – Integrated downhole/surface simulations are performed on different timescales. Short-term simulations (over a few days or months) permit production optimization based on a simplified reservoir simulation coupled with a detailed simulation of the wells and surface networks. For longer-term predictions relating to subsequent field development phases, we developed a unique and innovative proprietary solution called INTERSECT Network. This tool enables us to simulate a simplified representation of the surface networks directly in the new-generation INTERSECT reservoir simulator, of which Total is a co-owner.
A Vital Avenue of Research & Development
Integrated asset modeling is no longer merely an option that gives us a competitive edge; it is and will continue to be an essential tool for meeting the new challenges posed by crude oil price volatility, the increasing diversity of the resources to produce, and the Group’s environmental commitments.
Indeed, an integrated approach is imperative to obtain a comprehensive vision of CO2 emissions over the lifetime of our developments and minimize them thanks to our integrated models. It is also one of the keys to meeting a number of emerging challenges, including:
- Profitable development of marginal fields in the deep offshore by tie-in to existing production hubs;
- Production of unconventional resources based on simulations that accurately account for the crucial impact on surface networks of thousands of wells;
- Optimized development of the giant oil fields in the Middle East including the anticipated production of increasing volumes of gas above the bubble point that will have to be managed at the surface over time.
To complete this integration, we are directing our efforts at developing tomorrow’s integrated models, along the lines of INTERSECT Network, to accommodate a higher degree of complexity and more functionalities. To this end, we are focusing in particular on integrating the optimization algorithms developed by Total’s Refining & Chemicals branch into our tools.
In parallel, we have started to investigate deep learning technologies to combine the strengths of today’s integrated modeling tools with the potential power of data science.
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