Chasing Gas Asset Value Maximization: An Integrated Workflow Led by Reservoir Monitoring

Abstract

Currently the oil and gas industry is becoming more digitalized. The abundance of data varieties that are recorded has driven the industry to move forward from the conventional data management to more fashioned data acquisition. The field under study (Field A) is a deep-water gas asset, characterized by a complex internal architecture of many separate and discrete gas charged stacked sand bodies. Objective of this paper is to show the key role of the reservoir monitoring strategy, fully integrated in a multidisciplinary workflow that allowed to detail the reservoir conceptual model leading to the identification of valuable production optimization opportunities. Field A produces through smart wells with selective completions, equipped with permanent down hole gauge (one for each open layer) allowing Real Time Monitoring of the key dynamic parameters (e.g., rate, flowing bottom hole pressure) and implementation of surveillance actions such as selective Pressure Transient Analysis. A workflow is implemented to be able to describe each open layer performance integrating all available data starting from well back allocation verification through virtual metering implementation. Then, Inflow Performance Relationship per layer is used to back-allocate well production to each unit. Robust continuous update of material balance analysis for each layer allowed to verify alignment between the geological gas volume in place and the dynamic connected volume, leading to update coherently also the dynamic model. Comparison between geological gas volume in place and dynamic connected one triggered a revision of geological modelling, reviewing seismic uncertainty and facies modelling, trying to embed dynamic evidence. Among parameters taken in account, layers internal connectivity resulted as the most impacting one. The revised model allowed to identify and rank residual opportunities on developed layers and possible additional explorative targets. The result of this screening led to the strategic business decision to plan an infilling well, with primary target the best unexploited sub-portion identified inside one of the analyzed layers together with other stacked minor targets. The expectation of primary target resulted confirmed by the data acquired in the new well drilled. Moreover, the real time monitoring workflow has been implemented in a digital environment for continuous automated update resulting in continuous reservoir monitoring and management. The successful experience on Field A proved the key role of a structured Reservoir Monitoring strategy as "drive mechanism" for a decision-making process extremely impacting on the core business. The automation of data extraction, will lead the way to an increasingly efficient use of "big amount" of data coming from real time monitoring, thus further improving the overall process of asset maximization opportunities identification.