Month: July 2009

Term used to define when a party farms into a project as per the terms defined in an agreement, and normally entails the farm-in party paying his pro-rata past costs as well as his future expenses. Alternatively, under a ground floor arrangement the farm-in party may only have to pay his pro-rata future expenses and all past costs will be borne solely by the farmor

The total acres or wells, as the case may be, in which a working interest is owned.

Joint ventures in an oil and gas production project look at the number of wells in terms of gross wells and net wells. A gross well is a well in which a working interest is owned. The number of gross wells is the total number of wells in which a working interest is owned

amount of rock in the trap above the hydrocarbon water contact

Means gross proved reserves plus gross probable reserves

The petroleum resource management system (SPE-PRMS) divides the estimates to 3 possibilities groups:

Low Estimate (P90) – There should be at least a 90% probability (P90) that the quantities actually recovered will equal or exceed the conservative (low) estimate.

Best Estimate (P50) – There should be at least a 50% probability (P50) that the quantities actually recovered will equal or exceed the best estimate.

High Estimate (P10) – There should be at least a 10% probability (P10) that the quantities actually recovered will equal or exceed the high estimate.

The meaning of the parameter “Mean” or “Gross Mean Resources” (average potential of gas or oil reserves, as applicable) (the “Mean”) is the arithmetic mean of all these possibilities, or “the arithmetic average of all the possible outcomes”

Refers to the mean potential of gas reserves available in a gas field, somewhere between the P90 and P10 estimates. If this mean potential is stated to be 140 bcm the proven reserves may be as high as 200 bcm or as low as 80 bcm, with further certainty achieved after appraisal drilling and other drillings have been carried out.

Calculation of the “Mean” is done by running X number of observations in a probabilistic model and dividing their sum in the number of observations (X). It can be defined simplistically as the total sum of 30,000 values divided by 30,000. if you run a probabilistic model 1000 or x times, sum up all outcomes and divide by 1000 or x, you have the arithmetic mean.

It is important to point out that the mean value is different to the “best estimate” value and could be higher or lower. If one compares “mean” and P50, the two results would only be the same for a symmetrical normal distribution of values, namely for a bell curve,