Uncertainty Analysis of a Giant Oil Field in the Middle East Using Surrogate Reservoir Model (original) (raw)
Related papers
Proceedings of SPE Annual Technical Conference and Exhibition, 2006
Reservoir simulation is routinely used as a reservoir management tool. The static model that is used as the basis for simulation is the result of an integrated effort that usually includes the latest geological, geophysical and petro-physical measurements and interpretations. As such, it is inherently a model with some uncertainty. Analysis of these uncertainties and quantification of their effects on oil production and water cut using a new and efficient technique is the subject of this paper. During the analyses of uncertainty, the Surrogate Reservoir Model will serve as an objective function for the Monte Carlo Simulation. In this study, uncertainties associated with several reservoir parameterts and their quantitative effect on cumulative oil production and stantaneous water cut are examined.
Journal of Petroleum Science and Engineering, 2010
This paper presents a new method to reduce uncertainties in reservoir simulation models using observed data and sampling techniques. The proposed methodology is able to deal with problems with a high number of reservoir uncertain attributes and includes the development of a probability redistribution algorithm using observed data. The use of Latin Hypercube technique in the construction of uncertainty curves that is a quantitative representation of the overall uncertainty of the problem studied was also proposed. Based on new probability distributions, selective samples are carried out through the Latin Hypercube technique. The methodology was evaluated using two case studies. The first one, used for validation purposes, is a simple reservoir with 8 attributes; the second one is a more complex case with 16 attributes. The results presented in the paper showed that the proposed methodology can be efficiently used in the integrated study of history matching and uncertainty analysis, providing a practical way to increase the reliability of prediction through reservoir simulation models reducing the uncertainty through observed data.
Uncertainty Analysis of a Fractured Reservoir’s Performance: A Case Study
Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles, 2012
Analyse d'incertitudes des performances d'un réservoir fracturé : étude de cas -Ces dernières années, l'industrie du pétrole a accordé une grande importance à la gestion et à l'analyse d'incertitudes des réservoirs. Le développement d'une méthode permettant de modéliser et de quantifier les incertitudes au cours des études de simulation de réservoir d'une façon efficace et pratique est clairement souhaitable. Des approches différentes telles que la méthodologie des surfaces de réponse (RSM ; Response Surface Methodology) et la simulation de Monte-Carlo ont été utilisées pour évaluer les incertitudes. Au sein de cet article, la méthode de surface de réponse est utilisée pour appréhender les paramètres les plus influents sur les changements en termes de chute de pression et de facteur de récupération, en ce qui concerne leurs niveaux pratiques d'incertitudes au cours du développement d'un modèle de réservoir fracturé. La présente approche est utilisée pour amplifier les paramètres significatifs et développer une équation substitutive compatible et plus réaliste en vue de la prévision de la récupération d'huile à partir d'un réservoir fracturé faiblement perméable typique. Le modèle substitutif permet à l'analyse de Monte-Carlo de déterminer les sensibilités et la quantification de l'incidence de l'incertitude sur les prévisions de production. Les résultats indiquent que la récupération d'huile est plus sensible à la pression de l'aquifère, à la perméabilité de fracture et à la hauteur de bloc. De plus, toutefois, l'interaction entre d'autres paramètres tels que la taille de matrice, la perméabilité de fracture et le volume d'aquifère a montré un certain degré d'importance au cours de cette analyse. L'analyse de Monte-Carlo prévoit un domaine de grande ampleur de récupération d'huile pour l'exploitation de ce champ.
Uncertainty-Aware Surrogate Model For Oilfield Reservoir Simulation
ArXiv, 2020
Deep neural networks have gained increased attention in machine learning, but they are limited by the fact that many such regression and classification models do not capture prediction uncertainty. Though this might be acceptable for certain non-critical applications, it is not so for oil and gas industry applications where business and economic consequences of wrong or even sub-optimal decision is quite high. In this work I discuss the application of deep neural networks as a framework for approximate Bayesian inference in oilfield reservoir simulation study. Surrogate models with different neural network architecture are proposed to speed up compute- and labor-intensive simulation workflow. Regularization tools such as dropout and batch normalization, variational autoencoder for regression, and probabilistic distribution layers are used to quantify prediction uncertainty. Monte-Carlo dropout approach is further applied to estimate uncertainty given by standard deviation values for...
Uncertainty Assessment of Production Performance for Shale-Gas Reservoirs
International Petroleum Technology Conference, 2013
Accurate assessment of uncertainty and optimization of production performance in shale-gas reservoir are critical for successful planning and development of shale-gas assets. Compared to the conventional assets, shale-gas reservoirs display significant and different challenges for flow simulation, particularly in modeling of multi-stage hydraulic fractures and transport of gas from micro or nano pores to the fracture network. Stimulated fracture half-length, spacing, conductivity (initial and also during later times, i.e., during production), diffusivity, as well as adsorption parameters are highly uncertain in practice which have a huge impact on recoveries. Thus, specific methods or treatments are needed for efficient uncertainty quantification and optimization of production for shalegas reservoirs, such as the handling of key controlling parameters of fracture geometry, diffusion, and adsorption/desorption. This paper presents an integrated workflow for uncertainty assessment for well production and field development based on a newly developed approach for modeling and simulation of shale gas production in multi-staged hydraulic-fractured formations. In this approach, fracture system is modeled using three different fracture groups: the primary fractures with known geometry, the secondary fractures created by hydraulic fracturing process, and the tertiary small fractures that contribute to the enhancement of diffusion rate. The transport mechanism of gas from micro or nano pores to fracture network is also explicitly modeled through molecular diffusion and convection. Experimental design (ED) and probabilistic collocation method (PCM) are used to systematically analyze the impacts of different uncertainty parameters on gas production. Key uncertainty parameters (heavy hitters) are identified, which can be used as guidance for the field data collection process in order to reduce key uncertainties. The technologies and workflow developed in this paper are shown to be able to improve the efficiency & accuracy in uncertainty assessment, as well as to optimize field development.
SPE Journal, 2011
Reservoir modeling and simulation are subject to significant uncertainty, which usually arises from heterogeneity of the geological formation and deficiency of measured data. Uncertainty quantification, thus, plays an important role in reservoir simulation. In order to perform accurate uncertainty analysis, a large number of simulations are often required. However, it is usually prohibitive to do so because even a single simulation of practical large-scale simulation models may be quite time consuming. Therefore, efficient approaches for uncertainty quantification are a necessity. The experimental-design (ED) method is applied widely in the petroleum industry for assessing uncertainties in reservoir production and economic appraisal. However, a key disadvantage of this approach is that it does not take into account the full probability-density functions (PDFs) of the input random parameters consistently-that is, the full PDFs are not used for sampling and design but used only during post-processing, and there is an inherent assumption that the distributions of these parameters are uniform (during sampling), which is rarely the case in reality. In this paper, we propose an approach to deal with arbitrary input probability distributions using the probabilisticcollocation method (PCM). Orthogonal polynomials for arbitrary distributions are first constructed numerically, and then PCM is used for uncertainty propagation. As a result, PCM can be applied efficiently for any arbitrary numerical or analytical distribution of the input parameters. It can be shown that PCM provides optimal convergence rates for linear models, whereas no such guarantees are provided by ED. The approach is also applicable to discrete distributions. PCM and ED are compared on a few synthetic and realistic reservoir models. Different types of PDFs are considered for a number of reservoir parameters. Results indicate that, while the computational efforts are greatly reduced compared to Monte Carlo (MC) simulation, PCM is able to accurately quantify uncertainty of various reservoir performance parameters. Results also reveal that PCM is more robust, more accurate, and more efficient than ED for uncertainty analysis.
Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs
Natural Resources Research, 2016
Uncertainty complicates the development optimization of oil and gas exploration and production projects, but methods have been devised to analyze uncertainty and its impact on optimal decision-making. This paper compares two methods for development optimization and uncertainty analysis: Monte Carlo (MC) simulation and stochastic programming. Two example problems for a gas field development and an oilfield development are solved and discussed to elaborate the advantages and disadvantages of each method. Development optimization involves decisions regarding the configuration of initial capital investment and subsequent operational decisions. Uncertainty analysis involves the quantification of the impact of uncertain parameters on the optimum design concept. The gas field development problem is designed to highlight the differences in the implementation of the two methods and to show that both methods yield the exact same optimum design. The results show that both MC optimization and stochastic programming provide unique benefits, and that the choice of method depends on the goal of the analysis. While the MC method generates more useful information, along with the optimum design configuration, the stochastic programming method is more computationally efficient in determining the optimal solution. Reservoirs comprise multiple compartments and layers with multiphase flow of oil, water, and gas. We present a workflow for development optimization under uncertainty for these reservoirs, and solve an example on the design optimization of a multicompartment, multilayer oilfield development.
SPE Europec featured at 79th EAGE Conference and Exhibition, 2017
The high level of uncertainties during early phases of oilfield projects makes economic decisions challenging. One effective way to reduce uncertainties is gathering reservoir information from dynamic sources, such as well tests and production logging. This data must be incorporated into reservoir characterization integrated studies to generate probabilistic simulation models (scenarios). The objective of this work is to develop a procedure to update reservoir simulation models during reservoir characterization including well test and production logs, aiming better production forecasts. The proposed methodology consists in generating phi vs. log(k) equations derived from well test and production logging interpretation to update the permeability distribution in probabilistic scenarios without losing geological consistency. We also establish criteria for selecting wells to be tested based on openhole log data. We evaluate the result of the well this data incorporation in a synthetic f...
Risk assessment for reservoir development under uncertainty
Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2004
Decision analysis applied to petroleum field development is always strongly related to risk due to the uncertainties present in the process. Methodologies to quantify the impact of uncertainties are still not well established due to the amount of variables that have to be considered. The complete analysis usually depends on geological, economical and technological uncertainties that have different degrees of impact in the recovery process and may affect the decision process at different levels depending on the problem, reservoir characteristics, recovery mechanism and stage of field development. This paper shows several details of a methodology that can be applied to complex and simple reservoirs in a reasonable amount of time, discussing especially the influence of the model used to predict recovery, choice of production strategies to be used in the process, number of attributes and type of information necessary to obtain reliable results. A discussion of data integration among geology, reservoir engineering and economic analysis also is presented in order to reduce the amount of information necessary and time for the process. Some results are presented to show the advantages of automation and parallel computing to reduce the total time of the procedure where reservoir simulation is necessary for reservoir performance prediction.
Uncertainties in Reservoir Fluid Description for Reservoir Modeling
SPE Reservoir Evaluation & Engineering, 1999
Summary The objective of the present paper is to communicate the basic knowledge needed for estimating the uncertainty in reservoir fluid parameters for prospects, discoveries, and producing oil and gas/condensate fields. Uncertainties associated with laboratory analysis, fluid sampling, process description, and variations over the reservoirs are discussed, based on experience from the North Sea.