Aggregation of units

Preface

The prospect A was identified using a 3D seismic survey in a deep-sea environment. It represents a four-way structural closure bounded by a fault to the northwest. The goal of this study was to estimate the volume of potential hydrocarbon resources and assess the associated risks, enabling a decision on whether to proceed with exploration.

Initial analysis did not reveal any features, such as "bright spots" or "flat spots," which would indicate potential fluid saturation or contacts. Therefore, in the first evaluation step, the concept of purely structural closure was used, acknowledging the significant uncertainty regarding the potential pay area. The second step involved a more detailed investigation to identify depositional facies within the potential accumulation and integrate them into a unified prospect.

General methodology

For undrilled prospects, the evaluation process typically involves two main components:

1. Chance of Success (COS): This refers to the probability that a potential accumulation will result in a discovery, or the risk of encountering a dry well.
2. Estimated Size of the Accumulation: This is the probabilistic range of the accumulation, assuming a discovery occurs.

The likelihood of a discovery (COS) is generally estimated based on the probability that all necessary components (risk factors) for hydrocarbon accumulation—such as a trap, seal, source rock, and others—are present.

The estimation of Chance of Success (COS) and probabilistic range of resource volumes may become challenging when it is required to aggregate several layers, facies or segments into higher-level prospects. In such cases, It becomes essential to accurately establish the complex relationships among the risk factors to obtain valid results. This capability has been integrated into a flexible technique in Geonomix.

In the case of Prospect A, two evaluations are compared: 1) the original concept based on a purely structural model, and 2) the concept that incorporates the aggregation of facies elements. The advantages of the facies-based evaluation, which employs aggregation techniques, will be highlighted below.

Structural closure model

The structural approach included estimation of the uncertainty range of the area using the structural map. The three cases P90, P50, and P10 were selected between the structure crest and the spill point. Additional parameters, such as net thickness, porosity, and oil saturation, were incorporated based on knowledge from analogous fields in the region.

The distribution of potential oil-in-place volumes shows a broad range of values, with a P10/P90 ratio of 5.66, which is typical for this type of prospect. However, the estimated chance of success (COS) is relatively low at 0.25.

A subsequent full-cycle evaluation of the prospect, which included production forecasting and economic assessment, suggested that the exploration project might be too risky. This conclusion was supported by a negative EMV value for the low estimate (P90).

As a result, the company was considering removing the prospect from its exploration list due to excessive uncertainty. However, the consulting geologist recommended continuing efforts toward more detailed analysis of the seismic data to delineate the potential deep-sea facies elements.

Deep-sea facies model

Facies delineation

The next step involved reprocessing the 3D seismic volume to enhance its amplitude characteristics, followed by a reinterpretation that included detailed attribute analysis. This process allowed for the development of a depositional concept of the prospect using a deep-sea facies model—specifically, a slope fan.

The facies elements include a distributary channel and two lobes, which clearly exhibit their distinctive features on the amplitude maps.

The evaluation of resource volumes and risks for these facies elements, when considered individually, showed relatively low chances of success and potentially limited commercial viability. However, aggregating the facies into a unified prospect could enhance both the overall chance of success and the resource volumes. This requires the use of calculation tools that can account for the interdependencies between risk factors.

Facies aggregation

If the risk factors are considered independently for the facies, the probabilistic summation of resource volumes will yield an unrealistically high chance of success (COS) of 0.6. This highlights the need to establish appropriate dependencies between the risk factors that accurately reflect the geological context and depositional architecture.

Five main factors collectively determine the chance of success for the prospect: 1) trap presence, 2) seal integrity, 3) reservoir presence, 4) migration pathways, and 5) presence of mature source rock. Establishing the correct dependencies between these factors is essential for effectively merging the facies elements into a unified prospect.

Risk dependency models

The following examples illustrate the risk dependency models associated to factors of reservoir presence and the occurrence of mature source rock. These models have been directly utilized in this exercise using the Geonomix program.

The dependency regarding the presence of a reservoir is straightforward: the existence of a channel is a prerequisite for the presence of either lobe. In other words, if there is no reservoir in the channel facies, there will be no reservoir in the lobes. This principle is known as the Dependable Chance Model.

The Total Dependency Model was applied to the presence of mature source rock. If mature source rock is present, there is a likelihood that generated hydrocarbons will migrate to the traps. Conversely, if the source rock is not mature, hydrocarbon generation will not occur, resulting in no opportunity to fill the traps.

Modeling process and result

The models of risk dependency discussed above were implemented in Geonomix using the Risk Dependency Matrix tool, as shown in the diagram below. This matrix allows for the visualization of risk factors for all units (or segments) and establishes the dependency models between these units.

The estimated chance of success (COS) and the probabilistic distribution of oil-in-place resources for the combined prospect of three facies (channel, lobe 1, and lobe 2) are summarized in the table below.

Comparing the facies case to the previous estimate for the structural closure model reveals the following updates: 1) the low estimate (P90) remains similar, while the median (P50) and high estimate (P10) have significantly decreased; 2) the P10/P90 ratio has decreased by 45%; 3) the chance of success (COS) has improved to 0.39.

It is especially important that the Expected Monetary Value (EMV) at the lower end (P90) is positive for the facies-model case. This indicates that Prospect A merits further review as a promising exploration project.

Conclusion

The original evaluation, which was based solely on structural factors, has resulted in significant uncertainties characterized by a wide range of values and a low Chance of Success (COS). As a result, the prospect was viewed as risky, leading to a negative Expected Monetary Value (EMV) at the lower end (P90).

Identifying depositional facies has not only enhanced the overall understanding of the prospect's geology but has also reduced uncertainty ranges and improved the project’s Chance of Success (COS) by almost 50%. This improvement has shifted the Expected Monetary Value (EMV) at the lower end (P90) into positive territory. This was accomplished using advanced aggregation techniques that account for risk dependencies in the Geonomix program.

Consequently, the originally downgraded prospect has transformed into a valuable asset in the company’s portfolio.