Defining lines between strict geologic and geophysical disciplines have become more blurred over the past decade. The tools available to properly characterise the subsurface have been made easier to use and workflows that might have required two specialists have been combined and redefined.
The uncertain economic environment within the Oil & Gas industry has driven efficiency improvements across the board, requiring companies to optimise the output that their employees can achieve. In large part, the rapid pace of technological advancement has supported this effort. Every so often, a new tool is developed that transforms the landscape of established working practices and these ‘disruptive technologies’ help to further drive efficiency, enabling the workforce to perform better, faster and more cheaply.
This year, S&P Global is proud to announce the development of a new module as part of its Kingdom Seismic Inversion package that will empower geologists to perform accurate characterisation of the subsurface, enabling them to distinguish different rock properties in their prospects, as well as being able to separate rock from fluid and then to differentiate the fluid content of reservoirs between gas, oil and water.
This tool has been developed in the format of a software Wizard, that is easy to use, quick to process, and is underpinned by a robust algorithm that provides strong confidence in the results. This technology will transform established workflows by complementing existing technology provided by other packages, to provide a quick-look feasibility tool that will reduce result time, down from several months to several weeks.
If you would like to know more about what you can now achieve with this technology, then sign up to our interesting and informative webinar that discusses our recent developments airing on the 26th May at 4pm BST/10am CDT (and is also available to watch On Demand thereafter) and see how this tool can empower you to more accurately read between the lines and better understand your seismic data.