Forecasting for Reservoir Management sing a Machine Learning Implementation of Well2seis

Field management decisions are based on a sufficiently representative model of the subsurface. As such, the model has to be “reliable”; to increase this reliability and decrease the uncertainty, the model is updated; however, the update leads to the question of the well and seismic integration or assimilation. This question is almost a routine for well production data but is still an open question when it comes to the 4D seismic integration. Many tentative and approaches have been tried over the decades, without reaching any clear statements or practical solution or good practise to follow for a given real dataset. One of the challenges is to be able to extract relevant and accurate information from the seismic data. Recently, a new approach for uniting well and 4D seismic directly (well2seis) has been shown to yield potential in defining connected pathways in the reservoir.

We propose, with this project to further develop this promising technique by welding multiple monitor 4D seismic data to well production and data using an appropriate machine learning algorithm. By associating these two types of data we bypass the need for a model and directly forecast the field performance. In essence the network becomes the ‘model’ as it captures the spatial variations associated with fluid flow to the wells. Within this framework we also examine how the network may be used to capture uncertainty. The objective is to give a method that will provide a route to more direct and faster decisions in reservoir management. The development will be guided by access to a well-founded North Sea dataset providing frequent multiple seismic monitor surveys and the full well production. A simulation model will also be available to provide the necessary benchmarking.

Informal enquiries should be directed to the primary supervisor, Dr Romain Chassagne.

Applicants should have a first-class honours degree in a relevant subject or a 2.1 honours degree plus Masters (or equivalent). Scholarships will be awarded by competitive merit, taking into account the academic ability of the applicant. A list of available projects can be found at the bottom of this page.

Please complete our online application form and select PhD programme Petroleum Engineering, Petroleum Geoscience or Applied Geoscience. You should also include the project reference, title and supervisor names on your application. Applicants who do not include these details on their application may not be considered.

Please also provide a written proposal, at least one side of A4, outlining how you would approach the research project. You will also be required to upload a CV, a copy of your degree certificate and relevant transcripts and one academic reference. You must also provide proof of your ability in the English language (if English is not your mother tongue or if you have not already studied for a degree that was taught in English). We require an IELTS certificate showing an overall score of at least 6.5 with no component scoring less than 6.0 or a TOEFL certificate with a minimum score of 90 points.

Applicants MUST be available to start the course of study in October 2019.