INVESTIGATING THE IMPACT OF DIFFERENT RESERVOIR PROPERTY MODELING ALGORITHMS AND THEIR ASSOCIATED UNCERTAINTIES ON VOLUME ESTIMATION (GULLFAKS FIELD, NORTH SEA (original) (raw)
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International Journal of Engineering Applied Sciences and Technology
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.
Constraining uncertainty in volumetric estimation: A case study from Namorado Field, Brazil
Journal of Petroleum Science and Engineering, 2011
This paper describes the reservoir-modeling case of Namorado, an oil field located in offshore Brazil, the workflow, tolls and benefits of a 3D integrated study with uncertainties. A geological uncertainty study was initiated to identify and quantify the input parameters of greatest impact in the reservoir model. In order to rank reservoir uncertainties, a series of static models was built and a method to quantify the uncertainty associated with geological parameters was tested. The proposed workflow was developed in the Irap-RMS software and comprised the following steps: construction of the structural model; construction of the geological model; population of the geological model with petrophysical parameters, and uncertainty analysis. To construct the static reservoir model, the low, base and high cases of each uncertainty parameter were defined and used, and all combinations of these parameters were tested. The uncertainties related to the choice of parameters such as the variogram characteristics (type, range, and sill) involved in each geostatistical iteration were included into the workflow. The highest ranked contributors to uncertainty in Stock Tank Oil Initially in Place (STOIIP) were oil-water contacts, range of variogram used to calculate porosity in possible-reservoir facies, and 3D water saturation. The uncertainties related to the main parameters that affect the volumetric calculation were incorporated into the proposed workflow. The hydrocarbon probabilistic volume established for the Namorado Field varies from 92.07 to 134.04 × 10 6 m 3 .
2015
In general, standard distribution functions (normal, log normal, gamma etc.) of reservoir properties (porosity (Φ), water saturation (Sw), net pay (h) etc.) are taken as inputs in Monte-Carlo Simulation for volume estimation. Though this method takes care of the variability in input data set, it is likely that assumed standard distribution functions will not always fit to the actual variation of data. Therefore a customized distribution function which fits to the actual variation of reservoir properties is crucial for better accuracy in volume estimation.
European Journal of Engineering and Technology Research
Reducing uncertainties to the barest minimum before reserve estimation aids in making a better decision regarding field development. This study analyses uncertainty in hydrocarbon reserve estimation in Fuba Field using both scenario-based deterministic and stochastic methods. Two hydrocarbon reservoirs (A and I) were selected and mapped. Depth structure maps revealed fault supported collapsed crestal closures for both reservoirs. Uncertainty analysis was conducted using low case (P90), base case (P50), and the high case (P50) reservoir properties. On average, porosity, NTG and Sw are 31%, 89%, 10%, and 24%, 84%, 23% for A and I reservoirs. Hydrocarbon volumes recorded for the high case, base case, and low case using a deterministic versus stochastic approach are 30.52 MMSTB, 12.46 MMSTB, 4.57 MMSTB, and 18.52 MMSTB, 13.59 MMSTB, and 9.40 MMSTB for reservoir A, 58.87 MMSTB, 10.94 MMSTB, 1.51 MMSTB, and 25.56 MMSTB, 14.59 MMSTB and 7.63 MMSTB for reservoir I. While the base case was s...
Probabilistic approach for shale volume estimation in Bornu Basin of Nigeria
Journal of Petroleum and Gas Engineering, 2019
The gamma ray log has over the years provided the conventional means for shale volume (Vsh) estimation. Knowledge of Vsh is used in the prediction of petrophysical parameters like effective porosity and water saturation, which are the input parameters for the calculation of oil in place. Currently, many studies have been conducted on the Bornu Basin of Nigeria, to access its hydrocarbon potential. Unfortunately, the practice of using best gamma ray log value for the computation of gamma ray index, I GR , and subsequently Vsh estimation incorporates huge uncertainty in the estimated volumes. Uncertainty is best captured when estimates are represented in a possible range rather than single value measurements. To the best of our knowledge, this is the first time shale volume has been estimated from the gamma ray log using sampling techniques. The gamma ray log data of the two upper shaly intervals of the NGAMMAEAST_1 well, which penetrates the Gombe formation of the basin, were utilized for this study. The gamma ray log response of the zone of interest is the uncertain parameter in Vsh estimation. A histogram plot of the uncertain log data was used to assume the probability distribution of the data. In the MATLAB platform, Standard Monte Carlo (MC) and Latin Hypercube sampling techniques were used to model the uncertain log response using random numbers. Possible input log data generated from the distribution of the uncertain log data were used in the linear and non-linear models for shale volume estimation to run a series of simulations to determine the possible range of estimates with their probabilities. The Latin hypercube method has shown to be a quick and accurate alternative method to the standard MC method. The approach presented here sets a guideline for the implementation of a probabilistic approach for the volume of shale estimation.
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.
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 and reduction is a crucial part of stochastic reservoir modelling and fluid flow simulation studies. Outcrop analogue studies are often employed to define reservoir model parameters but the analysis of uncertainties associated with sedimentological information is often neglected. In order to define uncertainty inherent in outcrop data more accurately, this paper presents geometrical and dimensional data from individual point bars and braid bars, from part of the low net:gross outcropping Tortóla fluvial system (Spain) that has been subjected to a quantitative and qualitative assessment. Four types of primary outcrop uncertainties are discussed: (1) the definition of the conceptual depositional model;
3D Geostatistical Modeling and Uncertainty Analysis in a Carbonate Reservoir, SW Iran
2013
e aim of geostatistical reservoir characterization is to utilize wide variety of data, in different scales and accuracies, to construct reservoir models which are able to represent geological heterogeneities and also quantifying uncertainties by producing numbers of equiprobable models. Since all geostatistical methods used in estimation of reservoir parameters are inaccurate, modeling of �estimation error� in form of uncertainty analysis is very important. In this paper, the de�nition of Sequential Gaussian Simulation has been reviewed and construction of stochastic models based on it has been discussed. Subsequently ranking and uncertainty quanti�cation of those stochastically populated equiprobable models and sensitivity study of modeled properties have been presented. Consequently, the application of sensitivity analysis on stochastic models of reservoir horizons, petrophysical properties, and stochastic oil-water contacts, also their effect on reserve, clearly shows any alteration in the reservoir geometry has signi�cant effect on the oil in place. e studied reservoir is located at carbonate sequences of Sarvak �ormation, �agros, Iran; it comprises three layers. e �rst one which is located beneath the cap rock contains the largest portion of the reserve and other layers just hold little oil. Simulations show that average porosity and water saturation of the reservoir is about 20% and 52%, respectively.
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.