In 2009, the Kuwait Integrated Digital Field (KwIDF) project was established in the Sabriyah field in north Kuwait to boost production and reserves (Al-Jasmi et al. 2014). The goal was to help realize the vision of sustained oil production in Kuwait of four million barrels of oil equivalent per day (BOE/D) by 2030 . The project involved the creation of 11 integrated, automated workflows, and a real-time collaborative environment to help optimize production, reduce downtime, and improve reservoir management:• Reservoir visualization and analysis, and subsurface waterflood optimizer-helps enable the monitoring of subsurface health during the waterflooding process, and provides predictive reservoir optimization analysis and actions (Ranjan et al. 2013).By 2012, KwIDF had been deployed on 49 wells, representing a pilot that served as a proof of concept. By 2013, cumulative production gains of 756,000 barrels of oil were reported (Singh et al. 2013). While the gains were impressive, and management wanted to expand KwIDF, it was recognized that full deployment would pose significant challenges and, without a set of necessary changes, the value of KwIDF would not be realized.The key challenge facing management was to identify the appropriate operating model to deliver on the KwIDF vision and scale the program to accommodate future expansion across the rest of the organization. A transition and deployment assessment team was established by management to address this challenge.The transition and deployment assessment project produced a recommended operating model, a transition road map, change management strategy, risk and mitigation plan, and project charters to assist the program team and steering group in the deployment of KwIDF across the rest of North Kuwait.
Portfolio theory requires the decision maker to associate a project's reward potential with its risk profile to characterize its contribution to an investment program. Reward and risk must be quantified in a manner that enables comparison across the set of investment alternatives to ensure that the capital allocation process is optimized. This quantification becomes difficult when the opportunity set contains very different investments, such as an offshore oil field, an oil sands project expansion, and a refinery upgrade. The risk components are different for each, and the rewards have different time horizons. Nevertheless, the risks and rewards for these types of investment opportunities are clear and can be modeled using historical information and experience.Many companies have added shale plays to their investment portfolio, mixing one or more shale plays into an opportunity set. The risk and rewards of shale plays are gradually being understood. However, the ability to predict the economic performance in terms of production rate and reserve addition is not a science. Controllable and uncontrollable risks impact the expected reward. Controllable risks are quantifiable; however, uncontrollable risks, critical shale properties' distribution within the play and their effect on production, remain unpredictable before appraisal drilling.Evaluating shale play investments as part of a portfolio requires quantifying both types of risk. Uncontrollable risks require new methods and insights to understand and quantify. Key shale property distribution prediction, coupling well logs with geostatistics, enables the quantification of uncontrollable risks in a shale play investment. The method quantifies relations between key shale properties and well performance to improve predrilling production forecasts. This paper addresses the method's application within and between shale plays to optimize capital allocations. This enables senior management to deliver production growth, cash flow, net present value (NPV), and reserve replacement, within capital constraints, from a portfolio comprising shale assets.
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