Uncertainty Assessment of Hydrocarbon in Place With Geological Models Satisfying Gravity-Capillary Equilibrium

Abstract

In a reservoir, gravity forces lead fluids to stratify according to their density, and transitions zones appear around contacts (water-oil and gas-oil contacts). These transitions zones are gradual variations of saturation of each phase. These saturations are often generated using geostatistical methods leading to errors in the calculations of fluid in place. They must be calculated in a physical manner using capillary pressure curves associated with lithofacies. In this paper, a physical volumetric method to calculate the hydrocarbon in place with a high-resolution geological model is used on data coming from a synthetic field: the Brugge field. The uncertainties on hydrocarbons in place are evaluated using the Monte-Carlo method.

Oil Production Forecasting with Uncertainty Description Using Data Driven Proxy Model

“…These functions may be Relu, Sigmoid, Gaussian, or the sign functions. It should be mentioned that synaptic weight is considered for different neurons [4]. (2) Where g is the transfer function.…”

“…2). Learning is the most important ability of a neural network, and a neural network will be able to generalize, classify and foresee [4], [12]. In other words, because of having experienced, neural networks will have recognition ability.…”

“…Input datasets play a great role in studying uncertainty in production prediction. In addition, the uncertainty of input parameters makes it necessary to use a probabilistic approach [4], [6]. Although there are different methods for probabilistic data sampling, Latin Hypercubic Sampling (LHS) was chosen as an efficient sampling method [14].…”

“…Defeating the uncertainty in anticipating the production requires a huge number of simulations, which is unfeasible because of the long computation time. [4], [5]. Different techniques have been focused on, and among them, proxy models have received more attention.…”