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Shale asset investment decisions are difficult to model because they are portfolios of options under complex and time-evolving uncertainties. Sequential development decisions must balance short-term cash flow with long-term value creation. Typical assets consist of thousands of locations, making exact analysis impossible. Consequently, most decision-support tools are deterministic, or rely on simplified problem structures that significantly distort the decisions facing companies. In this paper, we present a new decision-support tool that improves decision quality by accounting for the full decision structure under uncertainty.
A core-based fracture prediction method is used to illustrate a value-of-information (VOI) decision-analysis protocol to inform completion decisions in tight gas sandstones. The ratio of late host-rock cement to available pore volume (PV), or degradation index, uses petrographic observations of cement distributions in core (including sidewall cores) to predict whether nearby but unsampled fractures (widths > 0.5 to 1 mm) are sealed (nonconductive) or open (conductive). Measurements from four sandstone plays suggest that the index correctly predicts open vs. sealed fractures with an accuracy in excess of 80%. The value added is calculated using Bayesian inference in which the accuracy of the index serves as the likelihood of the prior distribution of open fractures to assess the posterior probability that data represent a useful predictor of producibility. VOI of the prediction method is more than three times the cost to acquire the data. VOI is most sensitive to play-specific geologic and cost parameters including cost to drill, expected revenue from a successful well, cost of completion, cost of acquiring data for the index, and fracture probability distributions. The approach provides a way to value acquiring fracture data and points to a need for zone-specific production data in tight gas sandstones.
The methodology involves data processing, ingestion into databases, and data cleansing; application of automated machine learning (AutoML) to generate an accurate machine-learning model; and numerical optimization of decision parameters to minimize an economic objective. Constructing a Pareto front enabled decision makers to select a strategy that minimized cost without sacrificing too much of the initial 12-month oil production. Overview A typical way to assess the performance of an oil well is to compute the total cost per barrel of oil produced at ultimate recovery. However, in cash-constrained operating environments, including many unconventional plays, other measures, such as $/bbl of oil produced in the first year, may also be used. Because of the intensive nature of running detailed physics-based models to optimize decision parameters related to wellbore placement and well completion, a pure simulation-based optimization strategy typically is not feasible.
The methodology involves data processing, ingestion into databases, and data cleansing; application of automated machine learning (AutoML) to generate an accurate machine-learning model; and numerical optimization of decision parameters to minimize an economic objective. Constructing a Pareto front enabled decision makers to select a strategy that minimized cost without sacrificing too much of the initial 12-month oil production. However, in cash-constrained operating environments, including many unconventional plays, other measures, such as $/bbl of oil produced in the first year, may also be used. Because of the intensive nature of running detailed physics-based models to optimize decision parameters related to wellbore placement and well completion, a pure simulation-based optimization strategy typically is not feasible. The complete paper describes a data-driven machine-learning model that was selected to predict the oil production profile of a new well in an unconventional play because of its efficiency and its ability to directly learn from abundant historical data.
Summary At the heart of petroleum reservoir management (PRM) resides the challenge of selecting the best project from a group of feasible candidates in the presence of geological uncertainty. The challenge is particularly relevant in low-oil-price investment environments where many upstream projects are economically marginal and must be optimized. Companies are now more cautious. Investors are aware that they should consider not only the rewards of the projects but also their risks. For these reasons, the selection of projects to be implemented in the field should consider the geological risk and the capacity of the companies to tolerate it. In this paper, we introduce a decision-making model for active geological-risk management. The model is consistent with the utility theory framework and combines the mean-variance criterion (MVC) and stochastic dominance rules (SDRs) to guide the selection process. Two examples in the context of steam-assisted gravity drainage (SAGD) are presented. Introduction Considering the risk and reward tradeoff for decision making in the presence of uncertainty could be thought of as commonsense knowledge. However, this principle is seldom implemented in PRM, even though the decisions involve significant geological uncertainty. The geological uncertainty reflects a lack of knowledge in the geometry and properties of the reservoir.
In this paper it is presented how the application of the Decision Tree technique (DT) with different Utility Functions (UF) and the Certainty Equivalent concept (CE) can reveal the optimal level of financial participation (OLFP) of a given decision maker in risky projects for oil and gas exploration and production. The decision whether or not to participate in an upstream project may lead to either a one-company contract orand association of several companies, with the aim of distributing the risk to levels tolerated. With this in view, this article will apply the Decision Tree (DT) with five types of Utility Functions (UF) with their respective Certainty Equivalents (CE), and discuss the different results obtained, according to the type of UF used: exponential, hyperbolic tangent, logarithmic, square root and linear, the latter being used for the case of risk indifference and the others for decision makers with risk aversion. Each company has its particularities in deciding whether or not to participate in an oil and gas exploration and production project, such as the level os risk aversion or its estimate of reserves available for the next years, given the present production. Each utility function has a distinct behavior and each one of them is presented and discussed some utility functions suiting best each decision maker profile. Additionally, the application of different attitudes towards risk in the successive phases of an upstream project is discussed, as well as Multi-Attribute Utility Theory (MAUT), which can take advantage of the five types of utility function, each one capable of representing a different dimension of the same project (e.g.
"I'm right; you are wrong!" "Why is management doing that? How many times have you heard the following phrases or even said them yourself? Simply understanding the rationale behind the decision-making process can help you identify the importance of the issue, issues behind the "real" issue, and what really needs to happen to get things done. "I'm right; you are wrong!" We are an individualistic culture. In business situations, we have learned to value competition. It is not enough to win; we want to win badly! This competitive force can lead us to view decision making as another opportunity to win and be right. However, the ultimate goal should be to come to a quality decision that meets the long-term needs of the organization. "I'll just do it myself!" Sometimes group decision making seems like a pain, right? Quite honestly, it is more time-consuming, harder to keep the focus on the goal, and just plain exhausting. Communication can serve as a catalyst for change, which leads to ...
When I joined Statoil as a reservoir engineer in the mid-1980s, I was assigned to a team of experienced reservoir engineers with the task of predicting future production from the not-yet-decided development of the Gullfaks B field. Statoil had the latest and greatest in reservoir simulators, and we predicted 30 years of future oil production in double precision--a single production prediction for each year. We spent a lot of energy and time refining the model and used as many grid-blocks as we could, with the constraint that each simulation had to be completed overnight. Our production forecast was used as an input for the development decision, and the field was successfully developed. I have never checked, but I'm pretty sure the field never produced anything close to what we predicted.