Drilling and completion costs are key elements of development planning. Given recent increases in the price of oil and gas, exploration and production (E&P) companies are ramping up drilling activity. Capital costs could be expected to increase along with this increased demand for service companies. While the issue is dependent on a number of factors, one of the main drivers for drilling costs is simply the length of the well.

As seen in the figure below, drill costs increase with the total length of the well.

A simple model such as a straight line can be fit to this relationship, however, a semi-logarithmic function instead so that a straight line will be fit between the total well length and the logarithm of the (inflation-adjusted) drilling cost using an equation of the form $ln(Cost)\; =\; \alpha \; \times \; Length\; +\; \beta .$

## (Intercept) total_depth

## 6.5442375425 0.0003187194

The result is a positive relationship between well length (i.e., total measured depth) and cost, keeping in mind that the natural log of cost is being applied. The model is simple, and many important parameters are not considered which results in a fairly imperfect fit.

An alternate method would be a probabilistic fit. Using a full Bayesian statistical estimator that uses Markov chain Monte Carlo sampling, it is possible to capture the uncertainty of the forecast of this simple model. In the following plot, this darker blue band represents the probabilistic forecast covering the 50% confidence interval with the lighter (outer) blue band representing the 90% confidence interval.

Another significant factor in the price of drilling is the year in which the well was drilled. When wells of equivalent length are compared, recently-drilled wells are less costly. This might be an unexpected result given there is no specific element that differs from one year to another that might impact drilling costs (i.e., hole size, wages and salaries, well length, etc…). In fact, inflation might be expected to increase costs year-over-year therefore there are likely other latent factors at play.

It is not immediately clear what these latent factors might be. Two likely candidates are commodity prices and technological improvements.

When considering commodity prices, if the wells are grouped by year drilled, then plot the commodity prices (oil and gas) current as of drill date, a significant increase in the price of gas from the mid-90s through to 2008 can be seen followed by a price decrease until a rebound beginning in 2019. In contrast, the price of oil increased from the mid-90s through to 2014 (with a big spike in 2008). The oil price then dropped dramatically hitting lows in 2016 and 2020, rebounding each time.

As most Montney wells produce significant gas volumes, it could be expected that the price of gas would affect the demand for drill rigs and thus the price of drilling. Given that the price of natural gas, at least in the broad sense, has decreased since 2008 it could be expected that the drilling cost would also decrease. And that is what has been seen.

## Multi-Factor GLM

There appears to be at least two significant drivers of drilling cost: the length of the well and commodity prices. There are many ways to model this but choosing one such as Generalized Linear Models (GLMs) that allows multiple factors to be chained together and which could provide a correlated answer (i.e., such as “an increase in gas price of 20% is expected to increase drilling costs by x”) would be advantageous.

Starting with our initial model ($ln(Cost)\; =\; \alpha \; \times \; Length\; +\; \beta .$), we simply need to add terms for gas and oil price:

$ln(Cost\; )\; =\; \alpha $_{1} × Length + α_{2} × Gas Price + α_{3} × Oil Price + β

Because the model fits the logarithm of drilling cost rather than the cost itself, the new terms simply scale the prior estimates up or down. Speaking mathematically, adding in additional terms results in a multiplication (because adding two logs together is analogous to multiplying the unlogged equivalents).

Using this framework, gas and oil prices can be included and directly determine how scaling commodity prices by a multiple of x results in a change in drilling cost of y. For example, does doubling the cost of gas result in a doubling of drill costs?

First our new multi-factor models, (defined as “generalized linear models--GLMs) are fit to a number of resource plays (Montney, Spirit River, Cardium Gas, Cardium Oil, Bakken and Viking). Results are as expected: the price of drilling in all resource plays increases with well length and both the price of gas and the price of oil.

## X.Intercept. total_depth AECO_CAN_MMBTU Edm_Light_CAN_bbl

## Montney 5.929607 3.484000e-04 0.02402331 0.005116743

## Spirit River 6.645268 1.846165e-04 0.06220795 0.005604885

## Cardium Gas 7.391602 2.920706e-05 0.03593027 0.002024332

## Bakken 5.645327 2.776655e-04 0.03669319 0.004950398

## Viking 3.742968 7.073941e-04 0.10234891 0.009151538

## Cardium Oil 6.332467 1.521228e-04 0.04551063 0.005950550

Next, the change in drilling cost can be estimated if the commodity price doubles. By using a probabilistic model, the results are available as distributions which can be visualized as histograms.

## Conclusions

From this simple analysis, it appears that a doubling of commodity prices results in an increase in drill costs of between 30% and 100%.

While this sounds ominous, other than well length, all of the change in the cost of drilling was associated with commodity prices. As costs came down over time, it was assumed that the reason was declining commodity prices. One very significant factor that has not yet been considered is that there have been significant improvements in the efficiency of development in the past several years therefore decreases in drill costs cannot be entirely due to commodity prices. Furthermore, additional factors are also important, such as improved access to drilling locations due to the advent of pad drilling. Given these considerations, drilling costs should be less sensitive to commodity prices once these other factors are included in the analysis.

These factors, and others, will be discussed in future GLJ posts. For now, the current analysis can be considered an upper limit on estimates on the price elasticity of drilling costs with commodity prices. As a first order estimate, it can be assumed that commodity prices are responsible for half of drill costs meaning doubling of commodity prices results in an increase in drill costs of between 15% and 50%.

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