How do you model and simulate petroleum economics?

Surely, future oil/gas/product prices is the main component, and they are impossible to predict accurately. Many experienced managers don’t even try and use the current market data for project forecasts. For upstream projects, production forecast is also critical for economics assessment. But be aware that there has not been any development for which reserves/production estimates were proven accurate. Still, it needs to be done, and AI may become a new prophet to blame for wrong predictions.

For big energy/petroleum projects, raising finance is a very important part of the project development phase. Project promoters/owners, usually rely on financial models that simulate and estimate project cashflows far into the future and help them assess the viability of their projects. Debt finance is usually a key part of the source of funding for these projects. Good modelling and simulation of a range of cash flow scenarios can enable lenders to establish the size of the loans and lending rates (based on the riskiness of the project).

Great message about modeling.this process is one that requires an estimate of the following: 1) the geology must be complete in order to classify reservoir as : a) an undersaturated/saturated oil ,or oil/gas. This affects flow performance and yields quality of produced fluids. Also the determination of UNCONSOLIDATED SAND; this item can (and will ) impact any future reservoir performance. A DST, or flow performance. It is imperative to execute at least reliable flow performance to determine the reservoirs/well PRODUCTIVITY INDEX. 2) SAND also has a PI when mobile. If not considered in field data for simulation. 3) most , of not all, simulations must be executed only when reservoir type is known. Remember that oil/gas/water flow as unjt

I usually follow these steps: 1. Gathering data: Collecting relevant data on factors that influence the project, including oil prices, production costs, market demand, and geopolitical factors. 2. Building a model: This model should capture key economic drivers and their interdependencies. 3. Validating the model: Verifying the accuracy of the model by comparing its outputs with historical data. 4. Simulating scenarios: Using the model to simulate different scenarios. 5. Analyzing results: Evaluating the outputs to gain insights into potential risks and opportunities and identifying KPIs such as NPV, IRR, or ROI to assess project viability. 6. Sensitivity analysis: Understanding how changes in input variables affect the results.

El dato es el rey del modelado y la simulación. Un control de calidad de la información es clave para tener la mejor interpretación del fenómeno. El ajuste con el campo es vital ya que en muchos casos los resultados de la simulación no se ven en realidad en campo. Los pasos para el modelo y la simulación son casi Standar con los software comerciales existens sin embargo con la inteligencia artificial todo está cambiando, parten de los datos de campo.

The analyst should maintain a questioning approach, not solely relying on provided data. Involving a third-party consultant for independent analysis is crucial. It enables result comparison, offering fresh perspectives and enhancing data reliability through cross-verification. This collaborative approach mitigates biases, bolsters credibility, and adds assurance to the analysis, fostering confidence in the findings.

I usually follow these steps: 1) Define the objective and scope of work 2) Conceptual Design and Architecture 3) Coding 4) Testing/Debugging 5) Documentation 6) Communication