PetroVR Project provides development planning and production optimization tools for the expanded value chains required in many unconventional oil and gas resources.
Questions a PetroVR simulation can answer
- What reservoir uncertainties are most likely to impact success? What reservoir issues are less significant?
- What major value drivers will govern the success of our project?
- How much will well variability affect cost and schedules?
- What is the most we can pay per acre and still achieve our economic goals?
- How fast does drilling pace need to be? What number of rigs is optimal?
- How much will our learning curve impact drilling success, costs and schedule?
- Will new technologies impact our project (rates and schedules)?
- Can we reduce staffing costs through learning?
- And many more
Value Chain Simulation
Developing unconventional resources typically encompasses an expanded value chain, upstream and downstream, that typically incorporates new and emerging technologies. And modeling these business opportunities incorporates a wider array of professional and technical disciplines, in addition to the already diverse upstream business and technical disciplines.
|PetroVR Simulation of Expanded Value Chains|
|Subsurface Models||Drilling/ Production||Power Production||Surface Facilities||Economic Modeling||Environmental Factors||Downstream Processing|
PetroVR powers teams to move faster on unconventional resource plays by simulating an expanded business value chain with these capabilities:
- one integrated modeling platform to simultaneously evaluate inputs from multiple disciplines and value chains
- evaluates many competing scenarios quickly and pinpoints decision tradeoffs to understand operational and financial impacts
- expands the development planning horizon to include a wider range of opportunities and inherent risks
- project schedule and cost profiles become integrative outputs of the planning process, rather than static inputs
- reflects business rules, not hard-coded formulas, for greater model flexibility and robustness
- reconciles data quality within the modeling tools and workflows, not from the outside