Active reservoir management for improved hydrocarbon recovery

Hydrocarbon extraction for energy supply, or injection of CO2 to mitigate climate change, both require a detailed knowledge of the structure of underground reservoirs, and how they evolve in response to engineering decisions (when and where to inject or extract, how fast etc.). In this project we will develop a commercial version of a new statistical reservoir analysis technique discovered during a NERC grant as an aid to understanding and engineering such reservoirs. The new model is a way of extracting information directly from flow rate data already recorded at injector or producer wells without significantly adding to the cost of acquiring the data. It has proven successful in a number of field trials as a scientific concept, but it has been harder to prove commercial value without a version that could be run in trials by a practitioner. This project will provide such a platform, engage with potential end-users to make it as fit-for-purpose in its design, and extend the range of applications to a wider range of operational and commercial problems.

The method was developed initially as a means of calibrating the response of faults and fractures to fluid injection and withdrawal. Faults and fractures provide key barriers or pathways to fluid flow underground, and their response is critical in determining extraction rates of oil and gas and the long-term integrity of underground CO2 storage sites. At present the method can (1) identify if a geo-mechanical response exists (so that more costly full-scale geo-mechanical modelling exercise can be justified where necessary), (2) identify which mapped or unmapped major fault structures are dominating this response, (3) identify changes in this response in time-lapse mode.

A recent Market Research Report funded by a NERC pathfinder grant has (a) confirmed significant user interest in applying the method and in participating in early field trials and (b) highlighted the priority areas for potential applications in enhanced recovery of oil and gas.

Accordingly, the major deliverable of this project will be a free-standing desktop tool to enable users to apply our technique to these problems on specific test cases where conventional analyses have proven insufficient, allowing the market to decide on its utility. Initial functionality will be based on the priority areas identified by the market research.

The technology is based on establishing a multivariate regression model to forecast oil and gas production rates from past injection and production data. It provides a low-cost, optimised targeted search for the relevant well pairs that respond to each other, and quantifies the strength of the correlation. The scientific concept has been proven in a number of research articles and test cases, and in an independently-refereed, 'blind test' forecast of data not available to our team.

End users will benefit primarily from having independent access to the technology, and being able to condition the functionality of the tool after the project in an explicitly commercial environment, particularly early adopters. The tool will add an independent constraint to current reservoir models used to optimise the extraction of oil and gas, at little extra cost compared to acquiring the data or carrying out a conventional reservoir model. They will be able to evaluate the commercial value in the applications identified above, and to extend the range to new ones.

The main beneficiaries will be engineers and geo-scientists working in the energy sector, primarily to optimise and enhance the recovery of oil and gas, but also potentially to monitor the integrity of underground CO2 storage sites in the longer term. Key benefits to these users on completion of the project are:

- Major revenue generation from enhanced oil and gas recovery
- Significant cost savings from optimised production strategies
- New, independent information for operational decisions
- A user-friendly operational desktop tool to enable such decisions
- Improved computational speed through optimised high-performance computing

We have now had 4 user inquiries that have led to 2 consultancies, including the associated contract with NEXEN Ltd cited in this application, all from previous collaborators in the Research phase (ITF 'COFFERS' and BERR 'RESURGE' projects). Wider market penetration has been limited so far by lack of direct access to the tool itself by asset teams. This proposal addresses this major barrier head-on, by developing such a tool to enable user-led trials by early adopters at reduced commercial cost at the end of the project. The proposal builds on an attached market research report, funded by a NERC pathfinder grant, that has highlighted four main application areas that form the basis for the technical development plan.

The project will significantly improve the prospect of commercialisation of research work previously funded by NERC by (a) overcoming a major hurdle for user-led trials, and (b) by providing a service which will scale with market expansion.

The ability to enable user-led trials will add significant value to the technology in several ways, including

- providing market feedback on utility and real-world commercial value
- providing a key element of a proof-of-commercial-concept
- providing operational and market credibility if successful
- improving our negotiating position in seeking to scale the model with additional partners at the stage of financing for growth

Finally, detailed discussion with venture capitalist groups during our marketing exercise over the last two years has confirmed potential interest in financing for growth for the scaling stage, once a product exists and scaling is possible. Following investor events attended or hosted by ERI Ltd (the University of Edinburgh's commercialisation organisation) we have two outstanding invitations with US-based venture capital groups to make a full pitch at that stage. The current proposal is a necessary first step towards that objective.