Characterization and assessment of uncertainty in San Juan Reservoir Santa Rosa Field (original) (raw)
Related papers
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.
Uncertainty Analysis In Reservoir Production Forecasts During Appraisal And Pilot Production Phases
SPE Reservoir Simulation Symposium, 2001
The objective of this paper is to present a methodology for quantification of the impact of uncertainties in the economic evaluation of reservoirs. The methodology is based on the numerical flow simulation of many representative models of possible scenarios of the reservoir, through the combination of the attributes with uncertainty that characterize the reservoir. A set of data representing uncertainty values for each select attribute, and its associated probabilities are combined by the decision tree technique. After the simulation, a statistic treatment is done to obtain the expectation curve of production forecast and net present value. The procedure is done in a network of workstations, using parallel computing, with great reduction in the processing time and ensures the practical usage of this methodology. The methodology is applied to a real offshore field in Campos Basin - Brazil, in the appraisal phase, with few wells and few seismic lines. Introduction Production forecast ...
A Bootstrap Approach to Computing Uncertainty in Inferred Oil and Gas Reserve Estimates
Natural Resources Research, 2000
This study develops confidence intervals for estimates of inferred oil and gas reserves based on bootstrap procedures. Inferred reserves are expected additions to proved reserves in previously discovered conventional oil and gas fields. Estimates of inferred reserves accounted for 65% of the total oil and 34% of the total gas assessed in the U.S. Geological Survey's 1995 National Assessment of oil and gas in US onshore and State offshore areas. When the same computational methods used in the 1995 Assessment are applied to more recent data, the 80-year (from 1997 through 2076) inferred reserve estimates for pre-1997 discoveries located in the lower 48 onshore and state offshore areas amounted to a total of 39.7 billion barrels of oil (BBO) and 293 trillion cubic feet (TCF) of gas. The 90% confidence interval about the oil estimate derived from the bootstrap approach is 22.4 BBO to 69.5 BBO. The comparable 90% confidence interval for the inferred gas reserve estimate is 217 TCF to 413 TCF. The 90% confidence interval describes the uncertainty that should be attached to the estimates. It also provides a basis for developing scenarios to explore the implications for energy policy analysis.
Petroleum reservoir uncertainty mitigation through the integration with production history matching
Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2011
This paper presents a new methodology to deal with uncertainty mitigation using observed data, integrating the uncertainty analysis and the history matching processes. The proposed method is robust and easy to use, offering an alternative way to traditional history matching methodologies. The main characteristic of the methodology is the use of observed data as constraints to reduce the uncertainty of the reservoir parameters. The integration of uncertainty analysis with history matching naturally yields prediction under uncertainty. The workflow permits to establish a target range of uncertainty that characterize a confidence interval of the probabilistic distribution curves around the observed data. A complete workflow of the proposed methodology was carried out in a realistic model based on outcrop data and the impact of the uncertainty reduction in the production forecasting was evaluated. It was demonstrated that for complex cases, with a high number of uncertain attributes and several objective-function, the methodology can be applied in steps, beginning with a field analysis followed by regional and local (well level) analyses. The main contribution of this work is to provide an interesting way to quantify and to reduce uncertainties with the objective to generate reliable scenario-based models for consistent production prediction.
Probabilistic Reserve Estimation Constrained by Limited Production Data: An Integrated Approach
2005
Large uncertainties in structure and facies had been recognized in a major gas field in Pakistan after early production. The conventional reserve estimation methods had failed in providing a reliable estimate of gas-in-place (GIIP). It was possible to get a good history match of one-year production data using a wide range of GIIP through a slight and acceptable adjustment of porosity and permeability. The resulting possible range of GIIP could easily vary by a factor of 1.5. Structural uncertainties did not warrant volumetric estimates either. Material balance technique was questionable due to non-uniform drainage of the reservoir. Clearly these deterministic techniques of reserve estimation were not applicable at this stage of production considering the complexities of the reservoir. A probabilistic technique was therefore developed that addressed both static and dynamic uncertainties in an integrated approach while honoring the available production history. Combined treatment of static and dynamic uncertainties also ensured a better coverage of the entire sample space, thus making the probabilistic approach more reliable. Latin Hypercube Sampling (LHS) helped minimizing the number of simulation runs while providing a reasonable coverage of the sample space. Yet we ended up with almost 1500 simulation runs. The process of history matching, ranking and keeping track of all these simulation runs demanded an innovative workflow. A number of software tools were used to automate and optimize this process. Out of 1500 simulation runs, the 200 best runs having minimum objective function through history matching were selected. These runs were later used for production forecasting, for providing a range of reserves, and for sensitivity analysis to identify the most influential variables. Structure and NTG were identified as the two most critical variables for *GIIP while residual gas saturation was identified as an additional sensitive variable for reserves. Different geostatistical realizations had little impact on GIIP or reserves.
SPE Annual Technical Conference and Exhibition, 2011
The oil and gas industry uses static and dynamic reservoir models to assess volumetrics and to help evaluate development options. The models are routinely generated using sophisticated software. Very elegant geological models are often generated without a full understanding the limitations imposed by the available data or of the underlying stochastic algorithms. Key issues facing reservoir modelers that have been evaluated include use of reasonable semivariogram model parameters (e.g. range, form, and nugget), model grid size, and model complexity. Within the last decade there has been increased recognition that incorporating uncertainty into reservoir modeling yields better business decisions, generally decreases project cycle time, and enables better understanding of the impact of reducing specific uncertainties through additional data acquisition. The robust incorporation of a reasonable uncertainty description in static and dynamic models significantly improves business decisions. The use of stochastic earth models combined with well placement optimization workflows is likely to yield significantly optimistic forecasts. Well placement optimization should be based on property distributions derived via appropriate estimation methods rather than stochastic methods. The oil and gas industry is in general moving away from an "honor the data" paradigm to an "honor the data and respect/incorporate uncertainty" paradigm for reservoir modeling.
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.
Reserve Prediction for One Iraqi Exploration Oil Field Using Probabilistic Monte Carlo Method
13th International Conference on Recent Development in Engineering, Science, Humanities and Management, 2020
The estimation of the hydrocarbon reserve is an essential task for any production and exploration operations.Any exploration and production projects economic viability are based on the accuracy of their reserves estimates, which are made utilizing various input parameters as example porosity, water saturation and formation factor.These parameters can be derived from petrophysical data and well testes data.Because of uncertainties in the estimate of such parameter, both deterministic and stochastic methods must be used for estimating reserves. The input parameters for deterministic methods are certain individual value and thus the output represents single value. Because reservoir parameters are not standardized across entire reservoir, uncertainty reduce as data set increases. In such cases, stochastic approaches used, since random sampling can produce millions of random numbers and by properly analyzing this data set, these problems can be resolved very quickly.Simulation of Monte Carlo is an epitome of a stochastic method of this kind for estimating hydrocarbon resources.The success of a Monte Carlo simulation stochastic hydrocarbon reserve estimate depends on selecting model parameters andprecise controlling and understanding of model parameters that are important for successful outcomes. This study predicted how statistical distribution of porosity and water saturation affect the original simulated oil values for one Iraqi oil field, and discussed the results.
Assessing the Value of 3D Seismic Data in Reducing Uncertainty in Reservoir Production Forecasts
Proceedings of SPE Annual Technical Conference and Exhibition, 2002
Using three-dimensional (3D) seismic data has become a common way to identify the size and shape of putative flow barriers in hydrocarbon reservoirs. It is less clear to what extent determining the spatial distribution of engineering properties (e.g., porosity, permeability, pressures, and fluid saturations) can improve predictions (i.e., improve accuracy and reduce uncertainty) of hydrocarbon recovery, given the multiple non-linear and often noisy transformations required to make a prediction. Determining the worth of seismic data in predicting dynamic fluid production is one of the goals of the study presented in this paper. We have approached the problem of assessing uncertainty in production forecasts by constructing a synthetic reservoir model that exhibits much of the geometrical and petrophysical complexity encountered in clastic hydrocarbon reservoirs. This benchmark model was constructed using spacedependent, statistical relationships between petrophysical variables and seismic parameters. We numerically simulated a waterflood in the mo del to reproduce time-varying reservoir conditions. Subsequently, a rock physics/fluid substitution model that accounts for compaction and pressure was used to calculate elastic parameters. Pre-stack and post-stack 3D seismic data (i.e., time-domain amplitude variation of elastic responses) were simulated using local one-dimensional approximations. The seismic data were also contaminated with noise to replicate actual data acquisition and processing errors. We then attempted to estimate the original dis tribution of petrophysical properties and to forecast oil production based on limited and inaccurate spatial knowledge of the reservoir acquired from wells and 3D seismic data. We compared the multiple realizations of the various predictions against predictions with a reference model. Adding seismic data to the static description affected performance variables in different ways. For example, the seismic data did not uniformly improve the variability of the predictions of water breakthrough time; other quantities, such as cumulative recovery at a later time, did exhibit an uncertainty reduction as did a global measure of recovery. We evaluate how different degrees of spatial correlation strength between seismic and petrophysical parameters may ultimately affect the associated uncertainty in production forecasts. Most of the predictions exhibited a bias in that there is a significant deviation between the medians of the realizations and that the value from the reference case. This bias is evidently caused by noise in the various transforms (some of which we introduced deliberately) coupled with nonlinearity. The key nonlinearities seem to be in the numerical simulation itself, specifically in the transform from porosity to permeability, in the relative permeability relationships and in conservation equations themselves.