Let's shift to Reservoir Geostatistical Modeling. A reservoir is a complex subsurface volume described through static and dynamic variables obtained from detailed Reservoir Characterization studies, including core sample analysis, well-log interpretation, seismic surveys, and geologic/facies models.
Understanding and appropriately integrating multi-scale geological heterogeneities and the SEDIMENTARY PROCESSES involved in reservoir formation are key for more realistic reservoir modeling, particularly for mature fields.
When building reservoir models, there are deterministic and stochastic approaches. Creating a reliable stochastic model is crucial for accurately predicting reservoir behavior, especially in mature fields. The reasons could be:
Integrate the results and products of multidisciplinary studies to maximize their use and optimize the reservoir resources.
Estimate original hydrocarbon volumes.
QUANTIFY and ANALYZE uncertainty (UNCERTAINTY MANAGEMENT).
PROBABILISTICALLY, evaluate and quantify the STATIC CONNECTIVITY in the volume of the reservoir.
PROBABILISTICALLY, IDENTIFY in ADVANCE areas with potentially remaining oil (Bypassed-Oil) and locations for IN-FILL WELLS; additionally, NEW INTERVALS to be perforated, etc.
Carry out fluid-flow simulation on a set of reliable stochastically-ranked models-realizations (for example, using realizations corresponding to percentiles p10, p50, and p90).
Finally, support the field development plan and the DATA-DRIVEN DECISION-MAKING process.
Remember: Understanding sedimentary processes in reservoir formation is crucial for realistic modeling and accurate predictive models, especially for mature fields.
Here, we'll outline a general workflow for Geostatistical Modeling in common situations where the reservoir's facies model isn't accessible. In this example, A mature oil field located near Bogotá Colombia, the available information and data included well-logs (resistivity, GR, porosity, and water saturation), water contacts, depth interpretations of horizons and faults, and only a basic conceptualization of the sedimentary bodies' architecture.
A cross-section integrated into the PETREL Software Platform, see image below, was constructed. Later in the process, an Oil Original in Place or STOIIP synthetic log was included in the cross-section to calibrate the built model.
Based on the available data and a heuristic approach, a synthetic Electrofacies log was created to represent a fluvial environment. This log is depicted in the cross-section's third track from the left. Subsequently, the architectural elements such as channel sands, levee sands, and the background floodplain were modeled based on this log and used to populate the volume, as depicted in the map in the image below.
The porosity and water saturation data were used to create a petrophysical reservoir model and calculate fluid volumes. Next, multiple realization analysis was conducted to assess different statistical summaries and reconstruct the geostatistical realization corresponding to the p50 percentile. The map at the top of the target unit, depicted in the figure below, shows the lateral distribution of the Stock Tank Oil Initially In Place (STOIIP) and the the Water Oil Contact. The most promising areas for proposing new locations are highlighted in green/dark green on the map.
As demonstrated by this example, incorporating a conceptualization of the geometry of sedimentary bodies significantly boosts the predictive power and practicality of the resulting model. This enables activities such as planning and designing trajectories for new horizontal or unconventional wells, selecting intervals to be perforated, and estimating the volume of oil to be contacted by them. This highlights how leveraging and integrating even limited datasets allows for extracting invaluable knowledge that can be immediately applied to address pertinent business inquiries, particularly those related to mature fields.
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