BUILDING AND RANKING OF GEOSTATISTICAL PETROLEUM RESERVOIR MODELS

BUILDING AND RANKING OF GEOSTATISTICAL PETROLEUM RESERVOIR MODELS

Abstract:
The accurate characterization and modeling of petroleum reservoirs play a crucial role in optimizing hydrocarbon recovery and reducing uncertainties in oil and gas exploration and production. Geostatistical techniques have emerged as effective tools for building reservoir models by integrating various data sources and quantifying spatial variability. This abstract provides an overview of the process of building geostatistical petroleum reservoir models and discusses the importance of ranking these models for reliable decision-making.

The process of building geostatistical petroleum reservoir models starts with data collection, which includes well logs, core samples, seismic data, and production data. These diverse data sources are integrated using geostatistical methods, such as kriging, sequential Gaussian simulation, and multiple-point statistics, to capture the spatial distribution of reservoir properties. These properties may include porosity, permeability, lithology, and fluid saturation.

One of the critical challenges in reservoir modeling is the uncertainty associated with subsurface data. Geostatistical techniques allow for the quantification and propagation of uncertainty, enabling the generation of multiple plausible reservoir models. These models represent alternative realizations of the subsurface, considering the inherent variability and uncertainty in the data.

Ranking geostatistical reservoir models is essential for decision-making processes. Ranking methods involve assessing and comparing the models based on predefined criteria, such as history matching, production forecasts, or economic considerations. Model ranking helps identify the most reliable and representative reservoir models, providing insights into reservoir behavior, production potential, and risk assessment.

Several techniques are commonly employed for ranking geostatistical reservoir models, including history matching, production forecasting, uncertainty analysis, and economic evaluation. History matching involves calibrating the reservoir models to match the observed production and pressure data, ensuring that the models accurately represent the dynamic behavior of the reservoir. Production forecasting evaluates the performance of each model by simulating hydrocarbon production over time, considering different development and production scenarios. Uncertainty analysis quantifies the range of possible outcomes and identifies the models with the least uncertainty. Economic evaluation incorporates financial and economic parameters to identify the models with the highest economic potential.

In conclusion, geostatistical techniques provide a robust framework for building realistic and reliable petroleum reservoir models by integrating diverse data sources and accounting for uncertainty. The ranking of these models is crucial for decision-making processes, guiding reservoir management strategies, and optimizing hydrocarbon recovery. The selection of the most representative reservoir models helps reduce risks and uncertainties associated with oil and gas exploration and production, leading to improved operational efficiency and economic viability.

BUILDING AND RANKING OF GEOSTATISTICAL PETROLEUM RESERVOIR MODELS, GET MORE OIL AND GAS/PETROLEUM ENGINEERING PROJECT TOPICS AND MATERIALS

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