Active reservoir management for improved hydrocarbon recovery
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Geosciences
Abstract
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 carry out market research and determine user requirements prior to a separate application to develop a commercial version of a new statistical reservoir model discovered during a NERC grant as an aid to understanding and engineering reservoirs of oil and gas and underground storage sites for CO2.
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 a first step in developing such a platform. Here we will 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 technology is based on establishing a multivariate regression model to forecast oil and gas production rates from past injection and production data. The technique 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.
In this project we will carry out market research with the questions and objectives to be addressed listed in the Objectives section above. These work elements will enable us to submit a mature follow-on fund application by informing us of the size of the market, the specific technical problems of current interest, and to design and scope the template for a new software tool to be provided to a professional software engineer in a full follow-on fund application.
End users will benefit primarily from being able to condition the functionality of the tool, particularly early adopters to be identified by the market research. Ultimately 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. The aim is to provide a user-friendly platform to enable end-users to identify geo-mechanical effects, and to use the results to constrain parameters in conventional 'black oil' simulators, thereby improving reservoir description and conventional forecasting.
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 a first step in developing such a platform. Here we will 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 technology is based on establishing a multivariate regression model to forecast oil and gas production rates from past injection and production data. The technique 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.
In this project we will carry out market research with the questions and objectives to be addressed listed in the Objectives section above. These work elements will enable us to submit a mature follow-on fund application by informing us of the size of the market, the specific technical problems of current interest, and to design and scope the template for a new software tool to be provided to a professional software engineer in a full follow-on fund application.
End users will benefit primarily from being able to condition the functionality of the tool, particularly early adopters to be identified by the market research. Ultimately 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. The aim is to provide a user-friendly platform to enable end-users to identify geo-mechanical effects, and to use the results to constrain parameters in conventional 'black oil' simulators, thereby improving reservoir description and conventional forecasting.
Organisations
| Description | The aim of the project was to carry out market research on the commercial potential of the statistical reservoir model. This was achieved through direct requests, crowd-sourcing through specialist user fora and analysis of questionnaire returns and free-form comments. The results were positive, and the potential applications were identified in order of potential utility. This led to a successful bid for a full follow-on fund award with the same title. |
| Exploitation Route | In the long term the idea is to mine existing data on flow rate at injector and producer wells either to characterise hydrocarbon reservoirs, monitor performance and forecast future production rates independent of current physics-based models. |
| Sectors | Energy,Environment |
| Title | Statistical Reservoir model |
| Description | The statistical reservoir model predicts the response of the earth to injection and production of fluids during hydrocarbon production. It can be used to optimise hydrocarbon recovery, to identify connected well pairs and to identify hydraulically-reactive faults. |
| Type Of Technology | Software |
| Year Produced | 2015 |
| Impact | The software was embedded in a commercial grade workflow for the client and tested successfully. However, the company decided not to take up a licence after the trial. |