Headwave to present at NCS Exploration (May 10-11, 2017)

Headwave to present at NCS Exploration (May 10-11, 2017)

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The NCS Exploration Conference takes place at Scandic Fornebu, right outside of Oslo (Norway), on May 10-11, 2017. The backdrop for the conference is that few commercial discoveries are made despite many technical discoveries. Is this due to an exploration toolbox that is not sufficient for the proper de-risking of prospects? Discover new ways of working with Headwave & Earth Science Analytics' presentation on machine-learning.

Presentation: Machine-Learning to Reduce Uncertainty

Thursday, May 11, at 15:05 hrs

Presenters: Diderich Buch - Headwave, Eirik Larsen - Earth Science Analytics

Across industries and academia everyone agrees that risk and return are closely linked. It’s intuitive that new play concepts must involve more risk than near-field exploration in a mature area. What is equally well known is that risk is consistently poorly estimated and understood across most industries. On the downside, human bias makes us underestimate or misinterpret certain types of risk (80% of men believe they are better drivers than the average driver.) On the upside, human creativity and willingness to accept risk is crucial to order to make new, major discoveries - yet the question is which type of risk we accept and which risk can be mitigated.

Most people agree that computers generally do a much better job than humans on consistently determining underlying, stochastic risk given a vast pool of data. Why not use this to our advantage to break the pattern of disappointing exploration results over the past 5-6 years? 187 exploration wells resulted in <10 commercial discoveries during the last five years. 143 BNOK was invested in the 187 exploration wells that discovered only 224 MSm3 rec. o.e. in the last five years. More importantly, what was known and what was not known when those decisions were made?

The NCS contains both highly mature areas, with well understood behavior and areas that are far less explored and understood. In many ways, this is the perfect scenario for computers. The incredibly rich subsurface data and metadata on the NCS is perfect for machine learning. Machine learning, crafted correctly and properly cross-validated, does not contain subjective bias. Machine learning builds incredibly detailed, high-dimensional models using all the data as input. This implies both structured data (e.g. logs, cores) but also unstructured data (reports). Utilized correctly, and in the context of all available data, such AI-assisted workflows will enable petroleum geoscientists to better understand the tectono-stratigraphic development of sedimentary basins in general, and more accurately and quickly predict the nature and occurrence of hydrocarbons in sedimentary basins in particular. More specifically, these applications will enable geoscientists to apply probabilistic quantitative techniques to very large subsurface datasets, thereby facilitating a better understanding of the multidimensional and nonlinear relationships existing between some of the key geological properties (e.g. lithology distribution and properties such as porosity, fluid saturation, source-rock maturity and sealing capacity).

In addition such analysis and decision making require real-time interaction with data and software packages has to be designed for the level of interactivity required by the users. The best quality on decisions is obtained by users seeing a direct response to their parameterizations and choices. Many software packages, however, simply fall short when it comes to feedback with their traditional and inefficient “click, wait for result, change parameters, repeat” approach. Experts in geoscience should, rather, be assisted by software as opposed to the current approach where it is the software that drives the users. Software should keep data live at all times and provide entirely dynamic workflows that honor the acyclic nature of geoscience, improving productivity.

This talk provides insight into the process of machine learning on public data, further learning & updates from non-public data (within an oil company) and future use of data-driven analytics within the context of an application that easily facilitates both data-driven and human creativity exploration of multi-dimensional data at a regional, basin or prospect scale and the way such processes can be integrating into sophisticated cutting-edge SW technologies that are prepared to tackle the challenges of tomorrow and enable energy companies to drastically reduce costs and improve software and end-user efficiencies now and in the future.


For more information, please contact

Diderich Buch, Headwave, +47 922 90 446, diderich.buch@headwave.com

Eirik Larsen, Earth Science Analytics, +47 948 74 324, eirik.larsen@earthanalytics.no