Prospect Evaluation Services
Our Prospect Evaluation service brings together disciplines to build certainty in your reservoir structure, identify fluid zones, and determine the commercial viability of prospects.
To confirm the robustness of reservoir structure, Sensitivity Testing is applied to the reservoir depth structure to quantify uncertainty and its impact on closure and GRV. Similarly, Uncertainty Analysis of the reservoir thickness relative to contacts can be employed to understand the probability of commercial success (P10, P50, P90).
For an insight into fluid distribution, we recommend the use of a fluid attribute, such as the maximum fluid attribute from EEI. The findings from this can be confirmed and refined by our AVO Scanning technology which uses machine learning to identify AVO/AVA directly from pre-stack data.
In addition, elastic impedance data can be used to predict pay thickness between wells. Paya Energy offers several approaches which are dependent on the input data, for example if there are tuning effects, and the types of outputs required, such as probability estimations.
Uncertainty Analysis
Top Brent Depth Sensitivity
Top Brent Depth Sensitivity
Top Brent Depth Sensitivity
Multiple realizations of top reservoir depth using different velocity models. This method illustrates which areas of the depth structure are most sensitive to changes in modelling method. It can also be used to identify methods which are unsuitable.
Top Brent Depth Uncertainty
Top Brent Depth Sensitivity
Top Brent Depth Sensitivity
Uncertainty envelope map showing the maximum variation in depth to top reservoir. Similar maps can be created for reservoir thickness and include fluid contacts.
AVO Machine Learning
Forties Fluid Projection
AVO Identification from Gathers
AVO Identification from Gathers
Extended Elastic Impedance (EEI) projection is used as a direct fluid indicator to identify prospective hydrocarbon zones.
AVO Identification from Gathers
AVO Identification from Gathers
AVO Identification from Gathers
Class II/III AVO identified directly from angle gathers using SIP's machine learning algorithm. Direct analysis gives a good match to EEI. The result is more discrete and a better measure of where AVO response is cleanest and strongest.
Seismic Net Pay
Net Pay Band Limited
Net Pay Band Limited
Net Pay Band Limited
In band limited data, tuning produces bright amplitudes in impedance data. This results in a superficial Net Pay response.
Net Pay De-tuned
Net Pay Band Limited
Net Pay Band Limited
SIP Net Pay removes tuning effects to give a more accurate picture. In some areas the actual Pay is much thinner.