Introduction
This week’s column addresses the fourth and final driver along with its modeled metrics; namely, operational cost of oil production share ratios in Guyana. The fourth driver, like the previous three, separately and together combine to indicate broadly, the intrinsic affordability of the Buxton Proposal, my recommended Universal Basic Income mechanism, UBI, for addressing embedded poverty in Guyana,
Today’s presentation draws heavily from my previous somewhat elaborated and detailed review of the Inter-American Development Bank, IADB’s, Traversing A Slippery Slope, Guyana’s Oil Opportunity, 2020. In that study strong benefits were projected for Guyana, based on the results obtained from the application of the IMF-FARI model in the estimation of cost shares for oil production. Of note, it was also used for the estimating of Government Take, which was considered last week.
Based on the above stated context, I start today’s topic with a brief presentation of the FARI model in the next Section.
Fiscal Analysis of Resource Industries (FARI)
The IMF Website informs us that its FARI model is extensively used for fiscal design, revenue forecasting, as well as the overall project economics, along with operation and management of the petroleum resources sector as a whole. Based on its 2016, IMF Technical Note [994], the model became available formally in 2021. There are updated versions and interested readers should note, the IMF now offers an online training module for the FARI model.
As might be generally anticipated, the model presentation starts with what readers of this column might expect. That is, the typical life cycle of regular petroleum projects. This has been divided, as a rule, into the familiar four main petroleum project life cycle phases These are (i) exploration; (ii) development; (iii) production; and (iv) decommissioning.
Based on this project life cycle phasing, the IMF-FARI model can construct “a project-level modeling approach to estimate the government’s share of a resource project’s total pre-tax net cash flows.” This was utilized in the calculation of the previous [third] driver, which dealt with Government Take and revenue flows. In other words, it deals with the determination of economic rent, discounted terms at a rate equal to the minimum return to capital), along with the effect of the interactions among the different parameters constituting the fiscal regime.
From this viewpoint, the IMF FARI model ls a self-styled “simple Excel-based, discounted cash flow (DCF) model set up to reflect tax accounting rules and specific tax payments to the government.”
While the IMF-FARI financial analysis model for resource industries is perhaps better appreciated for its inputs to fiscal regime appraisal, as the IMF notes, it is also focused on project economics, annual project costs, appraisal, production, other economic concerns, like costs, prices, inflation, interest and discount rates, as well as financing assumptions. The model calculations are done on an annual basis (but could be adapted to other intervals if necessary, starting with the calculation of the project net cash flows before any fiscal impositions. Then, the model calculates each fiscal payment according to the fiscal regime parameters. These individual payments are added up to calculate the total government revenue from the project. The results are then used to estimate different indicators that allow for the evaluation of the fiscal regime along relevant criteria.
The IADB document is the product of two special departments; namely energy and infrastructure. It was supported by IDB Country Departments, the Caribbean and Guyana
Framing the exercise
For the remainder of this column I focus on framing the IADB’s modeling exercise in order to reveal its scope, purpose and limitations before attempting to report on its results next week.
First, the timing of the IADB modeling is important. The model results were released in August 2020. That is, just over one and a half years after Guyana’s First Oil [December 2019] At that early stage the emerging industry faced a number of ramp-up mechanical difficulties making estimating the daily production rate in barrels of oil per day, DROP, bbls/d problematic. Simultaneously, and compounding this limitation, only 8 billion barrels of oil based on 16 discoveries were being reported at that time by the Lead Contractor, ExxonMobil and its partners under the original PSA.
Further, as we shall note next week, the model necessarily used the project timelines, sequences and profiles as set out back then in 2020 by the Lead Contractor.
Second, oil production in Guyana is based on Floating Production, Storage, Offloading platforms, FPSOs. Project data worldwide indicate the following: FPSOs save time; reduce costs, provide safety, convenience, allow flexibility in field use, while having storage capabilities.
With FPSOs companies do not need to build permanent structures; they are leased. And with this they can store large amounts of oil and gas, increasing the commercial viability of hard to reach fields. This greater freedom, flexibility and versatility make FPSOs comfortably the preferred offshore production method in the oil industry today
Third, the IADB modeling exercise reports on the project economics. This is the area I focus on for today’s topic, the modeling cost share ratios. Project economics as used here encompasses the following 1] its economic impact analysis, including on income, employment, value added, government revenue, factor prices-economic rent, wages, interest rates; 2] cost/benefit analysis 3] cost effectiveness analysis as well as programmatic cost analysis.
Conclusion
Next week I consider the results obtained from the modeling exercise as they refer to the estimated cost shares ratios.
