Technical Guides

Oil & Gas Model Governance

Model governance for an oil and gas financial model requires an explicit connection between the reserve engineering function, which owns the underlying technical basis, and the finance function, which owns the financial model built on it, since the two are frequently maintained separately and can drift apart without a defined governance link. This guide sets out how oil and gas model governance should structure named ownership, change control triggers, and review cadence around this cross-functional dependency, extending the Knowledge Centre's general financial model governance discipline.

Oil & Gas Model Validation

Validating an oil and gas financial model requires procedures beyond general model validation practice: reconciling the model's decline and reserve assumptions against the current reserve engineering report, independently replicating any reserve-based lending borrowing base calculation, and verifying fiscal regime waterfall mechanics against the actual contract terms. This guide sets out these procedures as a step-by-step validation methodology, extending the general model validation discipline with the sector-specific checks this domain requires.

Oil & Gas Monte Carlo Risk Analysis

Monte Carlo simulation applies the general Monte Carlo technique to the specific, overlapping sources of uncertainty in an oil and gas financial model, reserve volume, commodity price, and production decline rate, running many combined iterations to produce a probability distribution of outcomes rather than a fixed set of discrete scenarios. This guide sets out how Monte Carlo simulation should be applied in this sector, how it complements rather than replaces the P10/P50/P90 reserve-based cases and defined sensitivity scenarios covered elsewhere in this domain, and where it adds genuine value over a simpler scenario-based approach.

Oil & Gas Sensitivity Analysis

Sensitivity analysis in oil and gas financial modelling applies the general sensitivity analysis technique to the specific variables that matter most in this sector: commodity price, production decline rate, capital and operating cost, and fiscal regime terms. This guide sets out which variables to flex, why decline rate sensitivity is distinct from price sensitivity, and why fiscal terms should be tested in combination with price given their frequent interaction through mechanisms such as an R-factor.

Oil Price Scenario Analysis

Oil price is one of the single most influential variables in any oil and gas financial model, and should be tested through a defined set of scenarios, a forward curve or bank price deck base case, and explicit upside and downside stress cases, rather than a single flat assumed price held constant across the model's full life. This guide sets out how oil price scenarios are constructed, the difference between forward curve pricing and a flat long-term assumption, and how scenario results should be presented alongside the base case rather than replacing it.

Onshore Project Models

Onshore project models are shaped by land access and surface rights, well pad-level economics across a typically larger well count than offshore developments, and the choice between pipeline and trucking takeaway for produced volumes ahead of pipeline connection. This guide sets out how onshore models are structured around these drivers, and how they differ from the facility-centric economics of offshore development.

Operational Due Diligence

Operational due diligence assesses a target's operating processes, supply chain, production or service delivery capacity, and management infrastructure — testing whether the business can sustain and scale its operations independent of the financial figures themselves. Its findings translate into cost driver assumptions in the standalone model and, in a strategic acquisition, into the integration cost and timeline assumptions that determine whether disclosed synergies are actually achievable.

Operations Model Assurance

Operations model assurance is the ongoing, genuinely independent function verifying that an infrastructure operations financial model remains conceptually sound, correctly implemented, and tracking actual outcomes over an asset's multi-decade life. This guide covers what distinguishes assurance from a one-time audit, why independence must be genuine rather than nominal, and how assurance should be structured to remain relevant as an asset moves through successive lifecycle phases.

Operations Phase Financial Models

The operations phase of an infrastructure asset lifecycle model covers the steady-state period between construction completion and the asset's next major renewal event: recurring revenue, operating cost, routine (as opposed to major) maintenance, and the working capital cycle this generates. This guide covers how to structure the operations-phase module of a lifecycle model, how it differs from the construction-phase module that precedes it, and how it should be built to receive renewal-cycle capital events without losing its own internal consistency.

Operations Scenario Analysis

Operations scenario analysis tests an infrastructure asset management financial model against a defined range of alternative futures, different funding levels, renewal timing assumptions, and performance outcomes, rather than relying on a single base case. This guide covers which scenarios an operations financial model should test, how scenario results should be structured and compared, and how scenario analysis differs from a simple sensitivity table applied to a single input variable.

Operations and Maintenance (O&M) Cost Models

A power project's operating cost should be built with an explicit fixed and variable split, appropriate escalation applied to each, an explicit major maintenance reserve for periodic large component replacement, and a cost structure matching the actual O&M contract type — fixed-price full-service versus time-and-materials. This guide covers how each of these O&M cost mechanics should be modelled, extending the general operating cost build already introduced in the base power project model structure.

Outpatient Clinic Models

Outpatient clinics generate revenue from scheduled, lower-acuity visits with materially lower per-visit cost intensity than inpatient care, and their financial model is driven primarily by provider productivity and scheduling utilisation rather than bed capacity or case mix. This guide covers how to model outpatient visit volume from provider capacity and scheduling efficiency, and how outpatient cost structure and margin dynamics differ from the inpatient model.

PPA Risk Assessment

A power purchase agreement's value to a project depends not just on its headline price, but on the offtaker's credit quality, the pricing formula's actual complexity and sensitivity to external indices, the volume structure's allocation of shortfall risk, and termination or curtailment provisions that can end or reduce the contracted revenue stream before its stated tenor. This guide covers how to assess each of these PPA-specific risk dimensions systematically.

Patient Volume Forecasting

Patient volume is the foundational demand driver of a healthcare financial model, and the correct forecasting method depends on service type: inpatient admissions, outpatient visits, and procedure counts each respond to different drivers and carry different capacity constraints. This guide covers demographic and referral-based forecasting methods, how physical and staffing capacity caps a volume forecast, and how to build a defensible, source-documented volume assumption rather than a simple trend extrapolation.

Performance-Based Contracts

A performance-based contract pays an infrastructure operator or service provider according to measured output or outcome performance, rather than reimbursing input cost, aligning the provider's financial incentive directly with the asset owner's desired service outcome. This guide covers how to model the payment structure of a performance-based contract: the performance indicator framework, how bonus and deduction mechanics should be built as live formulas rather than static assumptions, and how this contract type differs from cost-based and fixed-fee arrangements.

Petrochemical Financial Models

Petrochemical financial models centre on the steam cracker, the plant that converts hydrocarbon feedstock, ethane or naphtha, into base petrochemicals such as ethylene and propylene, and the further conversion of those base products into polymers such as polyethylene and polypropylene. This guide sets out how petrochemical models are structured around feedstock flexibility, product slate economics, and the frequent integration between petrochemical operations and refinery feedstock supply.

Pharmaceutical Manufacturing Models

Pharmaceutical manufacturing financial models differ from general healthcare provider models in being production- and product-lifecycle-driven rather than patient-volume-driven: batch production economics, regulatory approval milestones gating revenue recognition, and patent expiry (patent cliff) risk that can cause a sudden, structural revenue decline. This guide covers how to model batch production cost and yield, how regulatory milestone timing should be reflected in the revenue forecast, and how to model patent cliff exposure explicitly rather than as a smooth terminal decline.

Physical Climate Risk Models

Building a physical climate risk model requires translating hazard exposure, whether acute event-driven or chronic gradual change, into a financial loss figure at asset or portfolio level. This guide covers asset-level hazard exposure mapping, the distinct loss estimation methodology appropriate to acute and chronic risk respectively, and how hazard data is translated into a usable financial output.

Pipeline Financial Models

Pipeline financial models extend the segment-level midstream economics covered in Midstream Financial Models with the specific tariff methodology, capacity allocation structure, and debt sculpting mechanics that apply to a pipeline asset. This guide sets out how a pipeline model is structured around cost-of-service and negotiated tariff regimes, firm versus interruptible capacity contracts, and the project finance-style debt structuring these contracted revenues typically support.

Port Operations Financial Models

A port operations financial model represents throughput-driven terminal revenue alongside two structurally distinct asset categories: long-lived civil infrastructure (quay walls, breakwaters, channel depth requiring periodic dredging) and shorter-lived cargo handling equipment (cranes, yard equipment), each renewing on its own cycle. This guide covers how to build that operations-phase model, including throughput forecasting by cargo type and the periodic capital dredging requirement specific to maintaining channel and berth depth.