New visualisation and exploration techniques will offer geoscientists unprecedented access to knowledge stored in vast amounts of data, speeding up prospect analysis and increasing the success rate. Application of machine learning on subsurface interpretation is being developed to improve production and recovery rates, particularly impacting more complex reservoirs.

Vast volumes of data from seismic, well logging, drilling, production, geological, geophysical, petrophysical perspectives can be analysed easier and faster. Simulation of field development will improve and enable greater optimisation of well placement and injection. There will be greater accuracy in assessing how the reservoir should be and is performing.

We are digitally enabling the end-to-end process of well delivery, using a single source of data to integrate how we plan, design, work with the supply chain, execute and operate well activities globally.

  • Subsurface and wells engineer reviewing seismic image

    AI assisted seismic interpretation

    Interpreting subsurface faults and salt features in seismic surveys requires many weeks of intensive work from human analysts. Deep-learning models act as an intelligent advisor, rapidly scanning seismic data sets to identify geological features so interpreters can focus their attention on prospective areas. Reducing this time frees the interpreters to focus on areas where their expertise adds greater value. The new systems will accelerate analysis workflows and help to improve production and recovery rates, particularly for complex reservoirs.

    The FaultCrawler tool identifies common geological features such as faults. FaultCrawler is now used on seismic datasets across the world and is helping geoscientists to better characterise trapping configurations and fracture networks of potential drilling targets. The SaltCrawler tool uses machine learning to accelerate the velocity model building workflow by automatically interpreting salt boundaries, creating the velocity model and launching seismic migrations. With SaltCrawler, we now have an end-to-end workflow that can dramatically reduce turn-around time for a process that has historically taken 2-3 months.

  • Model of multiple well paths

    Well location optimisation

    Identifying optimal well location is a challenging task, even for an expert. It is all about balancing short term and long term returns in the light of uncertainty about reservoir behaviour.

    Well Location Optimisation technology can scan thousands of possible well configurations (locations, trajectories, completions) quickly to discover less obvious possibilities and help deliver improved field development plans. This enables engineers to better incorporate uncertainty about reservoir behaviour in their studies.

    In a recent deployment at the Bonga North field in Nigeria, the technology suggested a new well configuration leading to a 10% uplift in estimated ultimate recovery. This solution was incorporated in the field development plan. The new configuration is projected to deliver a significant benefit once the development is completed.

    The Well Location Optimisation has been used on around 15 fields including developments in the Gulf of Mexico, the Middle East and Asia and has identified new well locations that show a material uplift in the EUR or improved CAPEX efficiency.

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