Financial Analysis and Monte Carlo Simulation

1. Overview

Financial analysis is the final and arguably most consequential stage of project evaluation. It is where engineering parameters and cost estimates are translated into the economic language of investment decisions: present values, rates of return, payback horizons, and probability distributions.

Oil and Gas PDT's Financial Analysis module produces investment-grade economic evaluations for upstream oil and gas projects. The module takes capital expenditure estimates from the Cost Engineering module and constructs a multi-period discounted cash flow model that captures the full lifecycle of the asset -- from pre-sanction capital spending through first production, plateau, decline, and ultimately decommissioning and abandonment.

The module is purpose-built for upstream petroleum economics, with native support for production decline curves, fiscal regime terms, probabilistic analysis via Monte Carlo simulation, and commodity-specific market structures including LNG netback calculations.

2. Cash Flow Modelling

The foundation of the financial model is a multi-period annual cash flow projection. Each year of the project life is modelled explicitly, with revenues, costs, taxes, and capital expenditures calculated for each period and then discounted back to a common reference date to determine the present value of the investment.

Each period's net cash flow is discounted back to the reference date using the specified discount rate, producing a present value that is summed to yield the Net Present Value of the investment. The discount rate is user-configurable and typically reflects the company's weighted average cost of capital (WACC) or a project-specific hurdle rate.

3. Key Financial Metrics

The Financial Analysis module computes a comprehensive suite of economic indicators. Each metric captures a different dimension of project value, and together they provide a complete picture of the investment's attractiveness.

4. Production Decline Curves

Production forecasting is the primary revenue driver in any upstream economic model. Oil and Gas PDT implements industry-standard decline curve methodology for forecasting production from reservoir-drive mechanisms, supporting exponential, hyperbolic, and harmonic decline behaviors.

Users configure the initial production rate, initial decline rate, and decline type to match their reservoir characteristics. The platform also supports multi-phase production forecasts with gas-oil ratio (GOR) or condensate-gas ratio (CGR) assumptions that co-forecast secondary product streams.

An optional plateau period can be specified before the onset of decline, which is common for large offshore developments where processing capacity constrains early production. The platform handles the transition from plateau to decline automatically.

5. Monte Carlo Simulation

Deterministic financial models produce a single outcome from a single set of input assumptions. In reality, every input carries uncertainty. Monte Carlo simulation addresses this by running the financial model thousands of times, each with a randomly sampled set of inputs drawn from user-defined probability distributions. The result is a probability distribution of outcomes that quantifies the range and likelihood of every possible result.

Input Distributions

The platform supports multiple distribution types for stochastic inputs, including triangular, normal (Gaussian), and uniform distributions. Users define the distribution parameters for each uncertain variable based on their project-specific knowledge and expert judgment.

Key Stochastic Variables

Variables that can be modelled stochastically include commodity prices (oil and gas), initial production rate, CAPEX, OPEX, and decline rate. Users configure the uncertainty range and distribution type for each variable independently.

Interpreting the Output

The simulation produces probability distributions for NPV, IRR, and other economic indicators. Key output statistics include the P10, P50, and P90 values representing the 10th, 50th, and 90th percentile outcomes.

The platform also reports the probability of a negative NPV, the expected monetary value (mean NPV), and the standard deviation. Results are presented both numerically and graphically, with cumulative distribution function (CDF) and probability density function (PDF) plots available for export.

6. Sensitivity Analysis

While Monte Carlo simulation quantifies overall uncertainty, sensitivity analysis identifies which individual variables have the greatest impact on the outcome. This is critical for risk management: if the majority of NPV uncertainty is driven by oil price and production rate, those are the variables that merit the most attention.

Tornado Diagrams

The platform generates tornado diagrams that rank input variables by their impact on NPV. Each bar shows the NPV range resulting from varying a single input between its low and high values while holding all others constant. Variables are sorted from most impactful to least, providing an immediate visual answer to the question of which risks matter most.

Spider Plots

Spider plots show how NPV changes as each input variable is varied across a range while all other inputs remain constant. Each variable is represented by a line, with steeper lines indicating greater sensitivity. These complement tornado diagrams by revealing the shape of the sensitivity -- whether linear or non-linear -- and whether the NPV is more sensitive to upside or downside deviations.

7. Fiscal Regime Modelling

The fiscal regime governing a petroleum asset has a profound impact on its economics. Two identical projects can yield dramatically different returns depending on the tax structure, royalty rates, and government take provisions. Oil and Gas PDT supports three primary fiscal regime types covering the vast majority of global petroleum jurisdictions:

Within each regime type, the platform supports configurable parameters including royalty rates, cost recovery limits, depreciation methods, ring-fencing provisions, investment credits, and domestic market obligations.

The fiscal regime configuration is fully integrated with the Monte Carlo simulation engine, enabling users to assess how fiscal terms interact with commodity price uncertainty and project profitability.

8. LNG Value Chain

For gas-focused developments, the economic analysis extends beyond the wellhead to encompass the full LNG value chain. Oil and Gas PDT's LNG module models each link in the chain to produce an integrated economic evaluation.

Pricing Models

The platform supports multiple LNG pricing structures:

The LNG module integrates with the Monte Carlo simulation engine, allowing users to model uncertainty across the full value chain. Outputs include LNG-specific metrics such as unit cost of liquefaction, breakeven LNG price, and netback value at the wellhead, alongside standard NPV, IRR, and payback indicators.