Uncertainties in Reservoir Fluid Description for Reservoir Modeling (original) (raw)
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Evaluating Uncertainty in the Volumes of Fluids in Place in an Offshore Niger Delta Field
The purpose of this work is to evaluate the uncertainty in the volumes of fluids in place in Fault Block A (Segment 3) of the G-1 Sands in the OND field located offshore Niger Delta. This would aid in business decision making and limiting risks which impacts in the development of a successful hydrocarbon exploration and exploitation program. The evaluation was performed in three parts: The first part was executed by building a grid-based model of the reservoir using Eclipse & Petrel. A 100 x 60 x 4 grid was built & faults were created in the model which delineated the reservoir into six segments. The second part of the study involved the calculation of petrophysical properties that affect the volumes of fluids in place & distributing them in the model. This was done by assigning various probability distribution functions to porosity, water saturation and net-to-gross; and calculating STOOIP for the three hydrocarbon zones using Monte Carlo simulation. One hundred realizations of STOOIP were generated for each zone in the reservoir. In the third part of the study, these realizations were plotted as histograms to determine the P10, P50 & P90 values of STOOIP, and these values showed that there was a general decrease in these values for each zone with increase in depth. This methodology can be applied to other reservoirs for proper planning in new and existing field development, as well as the understanding of management risks.
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.
Uncertainty Analysis of a Giant Oil Field in the Middle East Using Surrogate Reservoir Model
Abu Dhabi International Petroleum Exhibition and Conference, 2006
Simulation models are routinely used as a powerful tool for reservoir management. The underlying static models are the result of integrated efforts that usually includes the latest geophysical, geological and petrophysical measurements and interpretations. As such, these models carry an inherent degree of uncertainty. Typical uncertainty analysis techniques require many realizations and runs of the reservoir simulation model. In this day and age, as reservoir models are getting larger and more complicated, making hundreds or sometimes thousands of simulation runs can put considerable strain on the resources of an asset team, and most of the times are simply impractical. Analysis of these uncertainties and their effects on well performance using a new and efficient technique is the subject of this paper. The analysis has been performed on a giant oil field in the Middle East using a surrogate reservoir model. The surrogate reservoir model that runs and provides results in real-time i...
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.
Journal of Industrial and Intelligent Information, 2014
Material balance analysis is an interpretation method used to determine the original oil and gas in place and to predict petroleum reservoir performance based on production and static pressure data analysis, also to evaluate the remaining reserves by applying the principle of material balance to rate-time decline analysis. Material balance techniques are widely used throughout all phase of reservoir development, providing a dynamic measure of hydrocarbon volumes and an estimate of key reservoir parameters. The purpose of this study was the quantification of the uncertainties in the estimation of original hydrocarbon in place. An extensive sensitivity analysis was conducted to provide an insight into the features that must be accurately determined in order to obtain the value of the OGIP. Common tools that are frequently used in the petroleum industry such as Material Balance and Monte Carlo were used in combination to support investment decisions for field development. To deal with this challenge, an automated concept has been developed using Petroleum Experts MBAL TM software. The results showed that the estimation of OGIP by material balance calculations was very sensitive to the pressure and aquifer models data uncertainties. Therefore, the error in pressure data identified as the most significant source of the uncertainty in material balance estimations. Errors in Porosity distribution and net pay thickness are the main source of uncertainty in the properties of reservoir characteristics. Permeability was the important sources of uncertainty but not significant. Finally, encroachment angel and compressibility were the parameter with less uncertainty on material balance calculations. Therefore, the significant of this study is to investigate the effect of reservoir data uncertainties on material balance calculation.
Reporting reliable results for hydrocarbon volume estimation is important for both economic analyses and making key decisions in reservoir management and development. Adequate facies and petrophysical modeling of static reservoir properties are key inputs for the derivation of a robust static reservoir model from which static volume is computed and inherent uncertainties are quantified. However, the choice of geostatistical algorithm for building the model depend on development and production maturity, degree of reservoir heterogeneity and the type, quality and amount of data. This study therefore aims at investigating the impact of the combination of stochastic and deterministic methods of property modeling on volume estimation and also perform uncertainty and sensitivity analyses to quantify uncertainties so as to aid exploration and production decision making process. Facies model were simulated/generated using both stochastic and deterministic algorithms. The resultant facies model formed an input for the petrophysical modeling process also using both stochastic and deterministic algorithms. For each combination, hydrocarbon pore volume was computed. Monte Carlo Simulation method was used to perform the uncertainty analysis where the low case (P10), mid case (P50) and high case (P90) was outputted The results show that a combination of Sequential Indicator Simulation (facies) with Sequential Gaussian Simulation (petrophysical) captured a large range of hydrocarbon pore volume for the twenty equiprobable realizations simulated while the combination of Truncated Gaussian Simulation with trend and Gaussian Random Function Simulation gave a limited range. A combination of the deterministic algorithm gave a single estimated and more pessimistic volume. Uncertainty analysis indicated that the facies modeling process and the combination of SIS_SGS algorithm have a higher impact on volumetrics.
Petroleum …, 2008
Estimates of recovery from oil fields are often found to be significantly in error, and the multidisciplinary SAIGUP modelling project has focused on the problem by assessing the influence of geological factors on production in a large suite of synthetic shallow-marine reservoir models. Over 400 progradational shallow-marine reservoirs, ranging from comparatively simple, parallel, wavedominated shorelines through to laterally heterogeneous, lobate, river-dominated systems with abundant low-angle clinoforms, were generated as a function of sedimentological input conditioned to natural data. These sedimentological models were combined with structural models sharing a common overall form but consisting of three different fault systems with variable fault density and fault permeability characteristics and a common unfaulted end-member. Different sets of relative permeability functions applied on a facies-by-facies basis were calculated as a function of different lamina-scale properties and upscaling algorithms to establish the uncertainty in production introduced through the upscaling process. Different fault-related upscaling assumptions were also included in some models. A waterflood production mechanism was simulated using up to five different sets of well locations, resulting in simulated production behaviour for over 35 000 full-field reservoir models. The model reservoirs are typical of many North Sea examples, with total production ranging from c. 15 10 6 m 3 to 35 10 6 m 3 , and recovery factors of between 30% and 55%. A variety of analytical methods were applied. Formal statistical methods quantified the relative influences of individual input parameters and parameter combinations on production measures. Various measures of reservoir heterogeneity were tested for their ability to discriminate reservoir performance. This paper gives a summary of the modelling and analyses described in more detail in the remainder of this thematic set of papers.
Characterization and assessment of uncertainty in San Juan Reservoir Santa Rosa Field
2005
This study proposes a new, easily applied method to quantify uncertainty in production forecasts for a volumetric gas reservoir based on a material balance model (p/z vs. G p). The new method uses only observed data and mismatches between regression values and observed values to identify the most probable value of gas reserves. The method also provides the range of probability of values of reserves from the minimum to the maximum likely value. The method is applicable even when only limited information is available from a field. Previous methods suggested in the literature require more information than our new method. Quantifying uncertainty in reserves estimation is becoming increasingly important in the petroleum industry. Many current investment opportunities in reservoir development require large investments, many in harsh exploration environments, with intensive technology requirements and possibly marginal investment indicators. Our method of quantifying uncertainty uses a priori information, which could come from different sources, typically from geological data, used to build a static or prior reservoir model. Additionally, we propose a method to determine the uncertainty in our reserves estimate at any stage in the life of the reservoir for which pressure-production data are available.
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.