Screening for opportunities
Preface
The company was presented with an exciting opportunity: a bidding round for oil exploration licenses had opened in a foreign country. Although information was limited, the area was situated within a mature basin, suggesting that exploration efforts might yield positive results. Time was of the essence, as they had only a short window to evaluate the licenses.
Among the options, one particular license stood out as particularly promising. Despite limited information on drilled wells and relatively sparse coverage of 2D seismic profiles, a preliminary evaluation of these profiles—incorporating adjustments to the velocity model—identified a potential target: a four-way closure anticline in the eastern part of the area. This potential trap is outlined across three profiles within the Jurassic play.
Single-layer model
Resources and risks evaluation. Based on the available data, the probabilistic ranges of reservoir and fluid properties were input into the Geonomix program. The distributions of properties (such as thickness, porosity, saturation, and recovery factor) were selected from the region's statistical libraries available in the program. This significantly reduced analysis time.
Risk parameters were established based on expert assessments and general knowledge of the region, supported by formal guidelines regarding factors such as trap and seal integrity.
The volumetric results for the single-layer model indicate a range of relatively low values for recoverable oil resources, which may be considered nearly marginal for the region.
Production forecast. The express assessment of oil production forecasts was based on analog wells from geologically similar fields in the region. Production history data for these wells were uploaded to the Geonomix program, where a probabilistic production model for the estimated ultimate recovery (EUR) of the so-called type well was developed.
Economic evaluation. The required number of wells for field development was calculated using Geonomix algorithms that matched probabilistic categories of recoverable volumes (as determined by geologists) with probabilistic production forecasts. Finally, probabilistic scenarios (2,000 realizations) of production forecasts, along with their corresponding drilling schedules, were sent for economic evaluation.
At this stage, a preliminary plan for project development was created, outlining the drilling of three wells during the exploration/appraisal period. This plan also involves constructing new facilities in the area, including oil treatment units, roads, pipelines, and more.
A conservative scenario was used for the oil price, starting at $60 per barrel and adjusted for inflation in subsequent years. Since the potential cost variations for the project are poorly understood in this region, probabilistic ranges have been applied to those factors. The calculated economic results for the one-layer case are summarized in the diagram below.
Results discussion. The initial results were somewhat disappointing, as the economics appeared negative in the conservative low case (P90). This raised doubts about the viability of pursuing this opportunity. The outlook seemed bleak, making the prospect of participating in the bidding appear unwise.
Multiple-layer model
New model. An assumption was made to model the potential presence of three reservoir layers, as analogous fields in the region typically contain between 2 and 5 reservoirs. The volumetrics and risks for two additional layers were calculated in the same manner as for the initial one. The process of probabilistic aggregation in the Geonomix program utilizes advanced algorithms and a flexible system for defining dependencies between risk factors for each layer. This new feature is referred to as the Risk Dependency Matrix.
Layers aggregation. An assumption was made to model the potential presence of three reservoir layers, as analogous fields in the region typically contain between 2 and 5 reservoirs. The volumetrics and risks for two additional layers were calculated in the same manner as for the initial one. The process of probabilistic aggregation in the Geonomix program utilizes advanced algorithms and a flexible system for defining dependencies between risk factors for each layer. This new feature is referred to as the Risk Dependency Matrix.
As shown in the table below, the aggregation process significantly increased both recoverable resources and the probability of exploration success (COS).
Forecast and results. A production and economic forecast was conducted for the aggregated multiple-layer case, following the same approach as for the single-layer case. The calculated economic results for the multiple-layer case are summarized in the table below.
| Category | Single-layer model | Multile-layers | Single-layer model | Multile-layers | ||||
|---|---|---|---|---|---|---|---|---|
| (PRMS) | Recovera ble oil resources, th. Bbl | Chance of Success (COS), % | Recovera ble oil resources, th. Bbl | Chance of Success (COS), % | NPV, mln USD | EMV, mln USD | NPV, mln USD | EMV, mln USD |
| P90 (1U) | 11 100 | 15 600 | -3.80 | -2.58 | 10.57 | 1.89 | ||
| P50 (2U) | 17 250 | 24 | 35 100 | 27 | 24.70 | 4.25 | 83.70 | 25.30 |
| P10 (3U) | 25 650 | 54 150 | 54.90 | 11.50 | 150.50 | 46.70 | ||
Notably, the economics have improved with the new geological model. Even under conservative low-end assumption (P90), the results remain positive, highlighting new opportunities for the analyzed area.
Conclusion
As a result of these new findings, the company recognized the need to participate in the bidding process. The positive outcomes energized the team, boosting enthusiasm for the bid preparation. With a revised strategy in place, the company felt more confident in its ability to capitalize on what appears to be a promising exploration opportunity.