Moving targets: forecasting global petroleum estimates

Paul Boughton

Reserve estimates are a critical component of the market value of publicly quoted oil and gas companies. This is due primarily to their use in asset depletion and impairment calculations.

Reliable estimates of ‘proven’ and ‘probable’ reserves are also crucial factors in planning corporate strategy: The projection of cash-flow, the determination of the use and extent of hedging/impairment derivatives, infrastructure planning, lending against projects and commercial valuations are all dependent on these figures.

The downward revision of 3.9 billion barrels of Shell’s reserves from proven to probable in 2004 not only contributed to a 7percent fall in Shell’s stock price and the downgrading of several other company’s reserves, but also to a flurry of regulatory activity following the inevitable breakdown of investor confidence. An industry-wide survey by Price Waterhouse Coopers reported that 80percent of petroleum companies believe their stock price to be undervalued.

Increased regulatory interest is rarely good news for those being regulated – unless it results in a so-called ‘level playing field’. Unfortunately, for publicly quoted companies, some 80percent of the world’s resources are in countries where information regarding reserves is with state-owned monopoly companies with no obligation to comply with disclosure procedures such as those demanded by the US Securities and Equities Commission (SEC). Furthermore, state-owned monopolies often restrict the disclosure of field-specific information by others through licence, partner and/or operating agreements.

Reserve estimation – referring by definition to a reserve which can be recovered economically – is to some extent a ‘moving target’. The economics of recovering a resource are closely related to the oil or gas price and can also be strongly affected by the pace of technical innovation. When one adds to this the disparate (albeit convergent) reporting and governance-related legislations (eg US GAAP, IFRS), as well as emerging demands for increased transparency, one may conclude that remaining compliant and managing risk (both financial and brand value) while planning and managing a sustainable business to maximise shareholder value will be a challenge in the years to come.

Common practice these days is to use the services of an independent reserves auditor to verify the calculation of proven and probable reserves, as this approach appears to satisfy the requirements laid out in the Sarbanes-Oxley act. Unfortunately this does little to satisfy investors’ demands for realistic information. It is largely unhelpful in developing information which can be used for strategic and operational management and does not remove a company director’s ultimate responsibility to report based upon ‘reasonable certainty’. The net effect of this approach is to encourage companies to be conservative in their booking of proven reserves – resulting in undervaluation and depriving shareholders from insights into that company’s management effectiveness.

Data used for estimation are collected throughout the life-cycle of a field, starting with exploration drilling. An exploratory well is drilled and initial physical data are derived. These include approximate subsurface depth, objective rock formation and the geological analysis of rock returned to the surface by the drilling fluid. This information is then combined with technical data obtained from seismic studies, wireline logs, core and fluid samples and possibly one or more drill stem tests.

Future field performance may be predicted in a number of ways, ranging from analogy with fields in similar geological settings, to simple material-balance calculations, to detailed finite-difference mathematical modelling of the processes involved. The latter method is most often applied to fields which have at least some production history. Predicted production profiles for various development scenarios are subjected to economic analyses and a view taken regarding reasonable certainty” prior to assignment of volumes into proven or probable categories.

Stated in its most simple form, a reserves calculation is as follows:

Reserve = (HCIIP x RF) – Np

HCIIP = Hydrocarbon initially in place (subject to geological uncertainty)
RF = Recovery factor (subject to both geological and development uncertainty)
Np = Volume of Hydrocarbons produced to date (known)

Uncertainty in HCIIP derives from reservoir architecture, extent, continuity, net porosity, fluid properties saturations and contacts, and depends on the quality and quantity of data.

Uncertainty in RF includes all of the uncertainties in HCIIP with the addition of ‘dynamic’ uncertainties, eg permeability, well productivity, well count, production mechanism.

Economic factors also play a significant part in the estimation of recovery factor – often it is technically feasible to produce 80percent of the HCIIP, but the cost of doing so is prohibitive unless oil is worth more than US$100/bbl.

In an attempt to reduce and quantify the spread of uncertainties, most companies use a system employing a statistical technique known as a Monte Carlo simulation. Instead of estimating single figures for the factors that go into the reserves formula, a best estimate range, having regard to all of the geology, is used. The reserves are estimated by use of the formula for the range of values – producing a range of possible estimates which are then analysed statistically and probabilities for each result assigned. This approach is reasonable as a sense-check, but unfortunately is not really rigorous (or reproducible) enough for detailed value or cost-benefit calculations.

The reliability and accuracy of figures produced from these initial calculations is highly dependant upon engineering skill and experience as well as the number of samples from which value estimates are derived. It is common practice for companies to create a static geological model, which is upscaled and transferred to a dynamic reservoir simulation model and used to predict field performance.

The process of validating reservoir simulation models with live data – known as History Matching – is however a complex and time consuming task requiring significant computational resources. Typically hundreds of reservoir simulation runs are involved. This ‘matched’ model is then used to predict future performance. Unfortunately these predictions are usually wrong, as they are based on only one out of many potential ‘solutions’.

Quantifying uncertainty

More recently, software has become available which applies advanced mathematical techniques to the energy industry, assisting engineers to validate their reservoir model rapidly. Such tools do not attempt to remove uncertainty from the process, but provide an accurate range of inherent uncertainty. So far, such solutions have been adopted to address the more traditional reservoir engineering applications, but the opportunity exists for such methods to be applied at other levels of the company.

For example: risk assessment and management; underpinning financial statements; modelling business strategy scenarios.

In all of these scenarios, any software solution must take into account the decision maker’s or the company’s attitude to risk; an acceptable risk to one company may be totally unacceptable to another.  

Adam Little is director of engineering & and consultant reservoir engineer, and John Merrell is a non-executive director, Energy Scitech (Scitech), Woking, Surrey, UK.


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