Comparison of Methodologies To Evaluate the Risk of Petroleum Fields

A risk analysis process can be applied to several p h ses of a petroleum field. The methodologies required in the decisionmaking process depend on the level of uncertainties , which vary according to the field phase. This work is foc used on the development phase and the decision process is based on probabilistic procedure to represent all possible s c narios of the reservoir. The uncertain attributes can be combined through derivative tree or Monte Carlo technique. The risk can be evaluated by the Net Present Value, which depends o the reservoir performance. The reservoir production pre diction can be obtained through numerical simulation or res pon e surface. Most of works consider only the applicatio n of these procedures and comparisons of these techniques are not well evaluated. The goal of this work is to apply the al ternative combinations of these techniques to petroleum field s and compare them to determine the result reliabilities. The combination of Monte Carlo and numerical simula tion is not viable, in some cases, due to the great numb er of simulations. The attribute combination by derivativ e tree can be an alternative, but can also yield a high number of simulation runs. Alternatives to speedup the proces s are to reduce the number of attributes and their discretiz ation levels or substitute the conventional reservoir modeling b y faster ones, such as the response surface, which is suited to valuate the impact of uncertainty on production forecasts. The contributions of this work are to: (1) to deter mine if the response surface can substitute the reservoir s imulator, (2) to evaluate the capability of the response surface to substitute the reservoir simulator in order to obtain the risk curves, (3) to provide a guide to select an adequate combination o f techniques according to the desired precision and ( 4) to determine if it is possible to reduce the number of simulation runs, maintaining the precision. Introduction Risk is always associated to a petroleum field, wit h minor or major intensity, depending on its life phase. In de velopment phase, the number of uncertainties is high, affecti ng strongly the financial results and requiring high investment . Demirmen (2001) states the risk associate in development dec isionmaking process involves suboptimal development and opportunity loss. For this reason, the decision-mak ing process in such phase must be probabilistic. Probabilistic approaches are common in exploration phase (Newendorp and Schu yler, 2000 and Rose, 2001). In development phase, the imp ortance of uncertainties increases significantly, mainly on the recovery factor, however probabilistic methodologies are not used frequently to assess the risk (Schiozer et al, 2004). Most of works present methodologies to assess risk in development phase and they present only illustrativ e examples of their application. Comparison of the performance of different risk analysis methodologies is not common in the literature. For this reason, the main goal of this work is to compare risk assessment methodologies in developmen t phase. The first step of a risk methodology is to combine th geological uncertainties. Two possible manners are: Monte Carlo and Derivative Tree techniques, resulting in many reservoir geological models. The second step is to calculate the value of some specific objective functions, suc h as Net Present Value and Cumulative Oil Production. These values can be obtained through numerical simulation flow o r faster simulation models. In the last step, the risk curve s are built through a statistical treatment (Figure 1). Figure 1. Risk assessment in development phase. SPE 107736 Comparison of Methodologies to Evaluate the Risk of Petroleum Fields Eliana L. Ligero, SPE, Fernanda V. Alves Risso, SPE, and Denis J. Schiozer, SPE, UNICAMP Uncertain Attributes Monte Carlo Technique Derivative Tree Technique Combination Objective Function Calculations Numerical Flow Simulation Risk Curves Risk Curves Statistical Treatment Faster Simulation Models Numerical Flow Simulation Risk Curves Risk Curves Statistical Treatment Faster Simulation Models