Summary Decisions to invest in oil and gas field acquisitions or participating interests often are based on the perceived ability to enhance the economic value of the underlying asset. The realization of value, however, is a function of numerous outcomes which, in combination, may or may not meet economic expectations. Common industry evaluation practice entails estimating most likely reservoir results, development plans and costs, prices and other factors within an economic framework. The risk of the opportunity is then assessed through sensitivities to key variables such as rates and reserves or costs, often through multipliers. Such an approach fails to explicitly acknowledge the uncertainties of the project or integrate the variables such that they interact as a system. The lack of understanding of the project volatility becomes more critical when the deal structure is negotiable and could be used to reduce the economic volatility. A multidisciplinary approach integrating reservoir engineering, operations and drilling, and deal structuring with Monte Carlo simulation modeling can overcome weaknesses of deterministic analysis and significantly enhance investment decisions. This paper discusses the use of spreadsheets and Monte Carlo simulation to generate probabilistic outcomes for key technical and economic parameters to ultimately identify the economic volatility and value of potential deal concepts for a significant opportunity. The approach differs from a simple risk analysis for an individual well by incorporating detailed, full-field simulations that vary the reservoir parameters, capital and operating cost assumptions, and schedules on timing in the framework of various deal structures. Introduction A host country government offered a bid tender for participation by a multinational company to improve reservoir performance in a giant offshore oilfield. The field was technically challenging to evaluate given its reservoir complexity, size (producing several hundred thousand BOPD), and complex infrastructure (several hundred wells, numerous wellhead platforms and process facilities and many miles of subsea pipelines). The proposed evaluation was further complicated by the unique nature of the bid terms stipulated under the tender and the competitive pressure. Initially, a deterministic technical evaluation was undertaken by creating a black oil reservoir simulation model to identify future reservoir performance established from a single proposed development plan. While this deterministic solution quantified the magnitude of the opportunity, it failed to define the uncertainty of the reservoir performance and capital requirements needed to understand the volatility of the project economics. Monte Carlo simulation provided a tool to acknowledge the technical uncertainties that would drive changes in the project economics. Integration of the technical and economic models provided the means to devise a deal structure that could hedge the contractor's risk and could be attractive to both the contractor and government under a wide range of outcomes. Process The risk assessment model was constructed in a PC-based spreadsheet with a commercially purchased add-in feature that performed the Monte Carlo simulation and captured the probabilistic outcomes. It was designed to be a dynamic tool that could estimate production performance with associated capital and operating costs to ultimately yield an economic analysis (see Figure 1).
Since ~400 BC, when man first replicated flying behavior with kites, up until the turn of the 20th century, when the Wright brothers performed the first successful powered human flight, flight functions have become available to man via significant support from man-made structures and devices. Over the past 100 years or so, technology has enabled several flight functions to migrate to automation and/or decision support systems. This migration continues with the United States' NextGen and Europe's Single European Sky (a.k.a. SESAR) initiatives. These overhauls of the airspace system will be accomplished by accommodating the functional capabilities, benefits, and limitations of technology and automation together with the unique and sometimes overlapping functional capabilities, benefits, and limitations of humans. This paper will discuss how a safe and effective migration of any flight function must consider several interrelated issues, including, for example, shared situation awareness, and automation addiction, or overreliance on automation. A long-term philosophical perspective is presented that considers all of these issues by primarily asking the following questions: How does one find an acceptable level of risk tolerance when allocating functions to automation versus humans? How does one measure or predict with confidence what the risks will be? These two questions and others will be considered from the two most-discussed paradigms involving the use of increasingly complex systems in the future: "humans as operators" and "humans as monitors."
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