IO 4 - Production Optimization and Subsurface IO (Phase 2)

IO 4 - Production Optimization and Subsurface IO (Phase 2)

This is the page for R&D projects in the IO Center.

The digitalization of the petroleum industry offers great opportunities for decision support tools. To fully realize the potential of increased amounts of data, new capabilities should be added to already existing tools, and in addition, new tools need to be developed. In particular, a range of data sources should be combined with mathematical models to compute recommendations in a variety of work processes. Hence, model supported tools provide a means to integrate across disciplines and silos. The overall objective is the optimal utilization of model based predictions to support short and long term decisions. Modeling and optimization tools facilitate faster and better decisions through structuring large amounts of knowledge and data.

This project consists of several on-going activites. A very brief overview is given below. Please explore our web pages for in-depth information.


Norne benchmark - a unique data set for assessing history matching and long term reservoir optimization techniques

The Norne field case will provide users with a benchmark case based on real data. Hence, this will be an extension to current benchmark cases which are all based on synthetic data. The purpose is to establish this case as a key benchmark for the petroleum industries. It will be used to evaluate and compare mathematical methods for history matching and closed-loop reservoir management.



Selecting well location

Well placement and design are key decisions in field develop. We have developed a new method for well location, with the potential of increasing recovery. This is currently tested in a pilot in collaboration with Total E&P Norge AS.


Integrating long term reservoir management decisions and short term operations decisions

Integrating long- and short-time horizons is an important but complex task in petroleum production. Reservoir engineers make decisions with a time horizon of months to decades, while production engineers are thinking in days and weeks. This activity seeks to investigate the boundary between these two silos and produce solutions to this challenge.


Daily production optimization

To determine the optimal production strategy in day to day operations is often difficult. An asset may have several bottlenecks, and balancing the tradeoff between these, with the goal of squeeze through as much oil and gas as possible, may definitely be non-intuitive.

On- and off-line simulators are commonly used for decision support, both to monitor and to perform what-if studies. During a what-if study, you are essentially conducting a “manual optimization”, testing alternative strategies and choosing the best one based on a predefined goal, e.g. maximum production of oil. In this activity we utilize several modeling and optimization techniques, jointly called SmartOpt, to automatically online, in a few seconds, generate optimal recommendations.


Field development with emphasis on subsea design

This activity explores the use of mathematical optimization as a means to improve design decisions for subsea system. As an example the may include recommendations on the timing, size and type of a new subsea separator. A key feature is the use of the integration of a lightweight reservoir simulator with network models.


Production optimization in shale gas systems

Shale gas is a game changer in the gas markets, and there are many reservoir modeling and operational challenges. We are attacking this by studying the use of model supported operations connecting the value chain from reservoir to export. Hence, model integration is important. The idea is to provide a method to improve current operational practice. Initial results are promising, and industrial feedback is good.


Dynamic simulator optimization for optimizing transient behaviour

Dynamic simulators are becoming increasingly used to model upstream petroleum production systems. They are used for forecasting system behavior and to perform what-if analyses. What is lacking, however, is their capability to offer recommendations. As an example; when ramping up production after shutdown a simulator can typically predict behavior when the operator presents a selected procedure. This challenge is addressed together with partners.


Smart eXcitations for well testing and monitoring

SmartX is a new way to test oil and gas wells that is faster than current technologies and can be conducted simultaneously on several wells. Furthermore, it can be used with existing wells without any specific preparation and with no significant production loss. SmartX is able to produce estimates much faster than conventional testing. For example, it does not test all the frequencies necessary to reproduce a build-up test, but only the faster ones. This is particularly relevant for parameter estimation in short- and medium- term optimization.


Other key information