A Decision Support System to Reduce Risk in Hydrocarbon Exploration
Authors: Fakier Nasier Mohamed , Al-Helal Hussein A. , Al-Zayer Ali A. 
Published: May 2016

Abstract
Hydrocarbon exploration is a complex, risk-based process based on uncertain scientific data. Decision makers are faced with different types of decisions during different stages of the exploration workflow. Relevant data reside in structured and non-structured repositories. Most data are spatially located and are connected through complex spatial relationships which make the data harder to model and visualize. This case study describes the features of a novel web-based decision support system used at Saudi Aramco for hydrocarbon exploration. This new software allows the regional geologists to outline the play and assess the risks and attributes. It guides explorationists through proper risk assessment and probabilistic uncertainty analysis of the proposed prospects and leads. All these assessments are compared against the play and regional trends. The risk and uncertainty of volumetric computations are handled using Monte Carlo simulation. Collaboration and knowledge sharing features help in reaching group consensus and reducing uncertainty. After drilling, the system captures post drill analysis which helps in identifying areas of poor predictive performances and possible remediation steps can be taken. This will in turn help in reducing risk and uncertainty of proposed prospects. Finally, the system provides an integrated view of the heterogeneous data and offers spatial analytical techniques such prospect historical analysis and prospect depth analysis. Explorationists can create ad-hoc queries across the entire dataset to uncover trends and anomalies and then drill down to the details. Using charting tools and GIS analytics the data can be further analysed to verify whether trends are real or anomalies explained.

Introduction
Although estimates of yet-to-find hydrocarbon resources have increased over the last few years, much of this is high-risk and high cost like deepwater exploration [1]. Driven by growing economies, demand is increasing, in turn necessitating an increase in exploration activities. As observed by Hailey [2], exploration requires large capital investments and is by nature high risk - this kind of activity should be supported by a reliable decision support system.
Given the amount of uncertainty, explorationists have many decisions before reaching the conclusion whether to drill a well, starting from play selection through seismic surveying and prospect evaluation. With the amount of capital and labour costs associated with exploration along with the large number of different options to be considered, a decision support system (DSS) is important for optimizing the targeted goals. The role of the DSS is not to make decisions, but rather to facilitate the decision making process by highlighting trends and patterns which provide a guide to reducing the risk element in these decisions.
In Saudi Arabia, the national oil company of Saudi Arabia, Saudi Aramco, has increased exploration activity concentrating on frontier and emerging areas [3]. These ventures are high risk, and could greatly benefit from a decision support tool.
Development of such an exploration decision support system would have to address a several issues within the computing environment. These would include multiple disparate data sources, addressing the spatial dimension of the problem and dealing with risk and uncertainty.