Shifting gears from MARKET BASKET ANALYSIS and RECOMMENDATION ENGINES for a little while, let's talk about Reservoir Geostatistical Modeling. A reservoir is a subsurface volume with a complex and heterogeneous internal structure. It could be described through a set of static and dynamic variables. Values for those variables can be obtained from detailed Reservoir Characterization studies: measure and interpretation of laboratory analysis of core samples; analysis of well-logs; interpretation of indirect measurements of seismic surveys; analysis of outcrops and analogs; definition of the geologic/facies models of the reservoir, etc.
Understanding and appropriate integration of multi-scale geological heterogeneities and the SEDIMENTARY PROCESSES involved in reservoir formation are key elements for more realistic reservoir modeling, particularly for mature fields.
Regarding the construction of reservoir models, there are DETERMINISTIC and STOCHASTIC approaches. It is well known that building a stochastic/geostatistical model with solid PREDICTION POWER is a very comprehensive and complex task. So, why a deterministic approach is not enough to describe reservoir complexities and performance? Why is so important to build a reliable STOCHASTIC MODEL of the reservoir, particularly for 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.
As mentioned above, understanding the sedimentary processes involved in reservoir formation plays a pivotal role in modeling them more realistically, allowing us to obtain solid PREDICTIVE MODELS: the ultimate goal of any modeling process is MAKING PREDICTIONS! Again, this is of paramount importance, particularly for mature fields.
In what follows, we describe a Geostatistical Modeling general workflow in the frequent situation when the facies model of the reservoir is not available. For this example, the information and data available were well-logs (resistivity, GR, porosity, and water saturation); Water Contacts and depth interpretations of the horizons and faults, and only a general conceptualization of the architecture of the sedimentary bodies. To begin with, an integrated cross-section, depicted in the figure below, was constructed in the PETREL Software Platform. (Later in the process, an Oil Original in Place or STOIIP synthetic log was included in the cross-section to calibrate the built model).
Using the data available and the heuristic as a starting point, assuming a fluvial environment, a synthetic Electrofacies log was built (see cross-section third track, from left to right). Then, the geometry of the architectural elements represented in the synthetic Facies Log – channel sands, levee sands, and the background floodplain– were modeled and used to populate the volume, as shown in the image below.
The petrophysical interpretation of porosity and water saturation was used to build the petrophysical model of the reservoir and calculate fluid volumes. Then, a multiple realization analysis was performed to evaluate various statistical summaries and reconstruct the geostatistical realization corresponding to the p50 percentile. The map below (at the top of the target unit) illustrates the Original Oil in Place lateral distribution and the Water Contact. The most prospective areas to propose new locations are colored in green and dark green.
Indeed as this example illustrates, including at least a conceptualization of the geometry of the sedimentary bodies substantially increases the predictive power and applicability of the resulting model allowing, for example, to plan/design trajectories of new horizontal/unconventional wells, select intervals to be perforated, and estimate the volume of oil to be contacted by them. This is an example of how reutilizing and integrating even sparse datasets allows for extracting priceless knowledge, which can be used immediately to answer relevant business questions, particularly regarding mature fields. The described workflow is part of one of our training courses currently available. For more details and prices of this and other courses, click HERE.
At MineaOil we always focus on the best approach: SCIENCE FIRST. For further INFORMATION regarding our available Consulting and Training solutions, please visit our WEBSITE. If it is of interest to your Company/Organization, we can make a short presentation of the products of our portfolio. Also, we can carry out pilot tests of any of our workflows with your datasets. To learn more, use the chat or visit our website to contact us.
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