Infill and Behind Casing Opportunities: opportunities can be readily classified into Behind-Casing-Opportunities (BCO) if accessible from existing wells, or Infill Opportunities if they reside outside of the reach of existing wells. In order to understand the true nature of by-passed oil, it is recommended to generate ROCM maps at the end of the No Further Activity (NFA) forecast. The Estimated UltimateRecovery (EUR) of these incremental opportunities can be calculated either deterministically through a target volume method, or using Predictive Analytics, estimated and benchmarked to historical reservoir performance via a machine learning methodology.
Notional Target Quantification
This module allows the identification of infill/in-well opportunities, quantified and ranked on multiple criteria.
Classical production decline driven forecasting is utilized.
Predictive Analytics Forecasting
Predictive (historically benchmarked) forecast, linked to the Predictive Analytics module and uses a machine learning algorithm
The ROCM can be directly utilized towards opportunity identification at the following levels:
- Behind Casing Opportunities (BCO): well re-entries
- Infill Drilling Opportunities
An example of this opportunity identification process is shown below. In this step, a volume target (Stock Tank Oil In-Place and Moveable Oil In-Place) and basic static and dynamic data are collected from the ROCM maps within the target area and/or at the location of the opportunity. This includes average phase(oil, water, gas) saturations, porosity, net pay, permeability, pressure etc.
Generating a Seriatim
The process of opportunity identification can be performed for all zones, thus creating a seriatim of opportunities. For each opportunity, production and ultimate recovery characteristics are estimated by integrating the saturation information, as well as productivity from neighbouring wells and/or mapped permeability. Identified uncertainties are taken into account in the risking of the in-well and infill opportunities.
A seriatim of opportunities with quantified recovery and production upside will be generated for the field.
Target risk assessment can be performed by iterating the ROCM process with different inputs (geological, production allocation etc.); a standard deviation map of remaining oil can be generated, and selected targets super-imposed; where standard deviation is high, the target risk is correspondingly higher; where it is low, then it means that risk is limited and the selected subsurface target is robust against the tested uncertainties.
Finally, using either a deterministic (direct interrogation of ROCM and application of flow calculation) or machine learning (Predictive Analytics) method, the future production characteristics of the selected opportunities can be determined. Through a sequential aggregation, a production forecast can be generated. Examples are shown below: