Mark Woolley
Maana

Mark Woolley is Digital Transformation Lead for Upstream Operations of Maana.

Prior to joining Maana, Woolley helped lead Digital Upstream at Accenture where he was responsible for accelerating transformation across E & P organizations. In this capacity, he defined the vision for the upstream business digital transformation, while identifying and qualifying industry and technology partners to enable a digitized enterprise for Accenture clients.


Based in the Netherlands, Woolley is responsible for helping organizations understand best practices around digitization specifically, how to use the Maana Knowledge Layer on Microsoft Azure to address business problems across the upstream value chain such as portfolio optimization, the lifecycle of wells, predictive maintenance, and health, safety and risk.


Woolley earned a degree in geology from the University of Manchester. Following graduation, Woolley began his career working globally for Halliburton Drilling Operations & Landmark. He spent eight years with Hitachi Data Systems in compute and file services, launching his international career supporting a number of industries, including oil and gas.


Wells Advisor Using Knowledge graphs for Oil Well Planning


Engineers within the oil and gas domain perform exhaustive due-diligence studies, reviewing the log and documents from previous field operations before an oil well drilling plan becomes final. Often the field documents are not in a standard form, lack an electronic format, and are in disparate locations. The lack of a consistent format, varying language syntax and semantics, or organization standards lends itself to missing insight and overlooking of critical information.


To combat the loss of potential insight we present our novel knowledge graph work, Wells Advisor, which takes a unique approach for analyzing technical documents to produce: recommendations, perform similarity matching, and understand lessons from drilling operations. We use a knowledge graph to capture new domain knowledge and operationalize its reuse. Our knowledge graph approach learns the context of new content, understands engineering jargon, normalizes information for consistency, performs contextual smart searches, and performs reasoning for recommendations. In this presentation, we will demo the system, provide the architecture the approach, and discuss what and which tools we use to support our system.