The intention has always been to simplify my analysis of the oil sector


Dear Editor,

Reference is made to Deryck Daly’s letter to the editor of Stabroek News dated October 18, 2022, with the caption: “Bhagwandin’s computer model seriously overestimates oil production OPEX and CAPEX”. Mr. Daly raised valid points in his letter. There really is no right or wrong answer. Thus, this letter is not intended to engage Mr. Daly in a debate per se, as I did with Professor Hunte and Ram et al, but to offer some clarification on the issues raised on the calculation. Mr. Daly may not have been aware of the full analysis which included the explanation of the assumptions used in the forecast analysis and the context for doing so. For Mr. Daly’s reference, the full report can be viewed on my LinkedIn page here: activity-6958434256062693376-ENYr?utm_source=share&utm_medium=member_desktop.

After reviewing the consolidated financial statements of EEPGL, HESS and CNOOC for 2021, operating costs represented 42% of revenue, of which depreciation and amortization expenses represented 14.41% of revenue. Typically, operating costs can range from 12% to 40% of revenue. Also, with the assumption that the operating cost remains relatively constant throughout the productive life, after recovering capital expenditure. For easier reference, below are the assumptions used in the base scenario financial model assumptions. The baseline scenario takes into consideration the economics of the four separately approved projects, after which the conclusions have been merged.

● Budget framework in the PSA:

 Royalty: 2% of gross income

Profit Oil: 50%

● Allowances:

 Amortization of capital expenditure (CAPEX): linear method of 3.4%

 Cost recovery cap: 75%

Profit Oil = Gross Revenue – Cost Oil (OPEX +

capital cost recovery)

Profit oil shared between the government and the

oil companies are:

Total government take = 2% of gross revenue +

50% profit oil.

The average price used in the base scenario

is $60.

Operating expenses (OPEX) for Liza 1 using

FY21 actuals were 42%.

OPEX for other projects is likely to be relatively constant at 30% (maximum) or 12% (minimum) throughout the productive life of the projects. This is due to a relatively inexpensive environment due to Guyana’s light, sweet crude combined with the technology employed by the oil companies which helps achieve greater efficiency.

A discount rate of 8% representing ExxonMobil’s weighted average cost of capital (WACC).

As the development/investment cost is recovered, the government’s share as a percentage of gross revenue is expected to increase from 14.5% to 37%.

The 75% cost recovery cap is divided into 30% for operating expenses and 45% for capital cost recovery.

To determine the payback period of the invested capital using the capital cost recovery rate, the payback method formula was used in the calculation.

The main reason I only used four projects in the forecast is that they are the only approved projects so far. So yes, I know of the others, but they weren’t included for that reason at this time. I’m aware that the Liza 1 permit is 20 years, but the reason I modeled the eleven-year recovery is because it’s based on the estimated reserves reported for each field, and assuming that production at nameplate capacity would be maintained throughout. But yes, Daly is right, the productive life can go beyond the 11 years up to 20 years where in the later years there will be a drop in production for each field or reservoir. In the scenario that I would have done, the full 20 years were not taken into account. Additionally, currently for both Liza 1 and 2, production levels have been optimized above rated capacity. I should also mention that a number of risks have also not been taken into account in the current forecast, such as the impact of environmental risk, operational risk, political, geopolitical and other economic risk factors. . I intend to include them in an updated forecast in due course, but as one can imagine, assumptions for these other factors would make the forecast much more complex and sophisticated – but much more practical and realistic.

In this context, the comments made by Mr. Daly are not necessarily wrong or right. In making these predictions, a number of scenarios with sensitivity analysis can be modeled with different assumptions. Appropriate justifications for assumptions are also important. In the forecast made, I only made one scenario for simplicity. Of course, as more fields are developed and approved, there is a lot more economic rent for the country, potentially, and the model can be updated. I intend to model a number of different scenarios to include some of the other assumptions highlighted by Mr. Daly in due course as well, and as time permits. Keep in mind that someone like Mr. Daly would appreciate this kind of work, to model a number of different scenarios using Excel (I don’t have fancy software yet to make it easier ) takes a tedious time.

However, as explained here, my main intention for now was to keep it as simple as possible. The objective was simply to reasonably determine, based on the current PSA, what Guyana’s revenues from the Stabroek block might be, and to have an informed basis for public discussion and debate on the subject. I found this absolutely necessary, especially since there is an army of commentators and critics of the PSA and other oil and gas issues, who have not taken the time to do a proper analysis to inform their public remarks and to at least have an intelligible discussion. Also, I agree with the opinion of a few others that a lot of time has been wasted by these so-called experts who continue to criticize the PSA but offer no meaningful input on how the country can properly utilize the resource for its development and economic prosperity.


Joel Bhagwandin


Comments are closed.