Modernizing Oil and Gas Royalties using Digital

Modernizing Oil and Gas Royalties using Digital

Did you happen to notice the modest press release on the use of distributed ledger technology to handle oil and gas royalty payments? I did, and frankly, the implications are too big to ignore.

Say again?

A Calgary-based oil and gas company, working with key stakeholders (another oil and gas company, a regional bank, and a royalty specialist), has created an oil and gas royalty smart contract on distributed ledger technology (DLT), and executed a payment based on the smart contract. You could treat this as much ado about nothing – smart contracts are fundamental to DTL solutions, no new revenue was created, and certainly innovation in the back-office in oil and gas attracts little more than yawns from the engineering world. Accordingly, there were a few modest press announcements of this event, but frankly, the implications are far-reaching, and worth a deeper dive.

What’s a royalty?

The oil and gas industry generally operates under some kind of hydrocarbon royalty regime. For those not familiar with this aspect of the industry, royalties are a kind of economic rent paid to the parties involved in hydrocarbon development and extraction. Take for example the case of a gas well located in a farmer’s field. The rural landowner will expect to be compensated for the inconvenience of having continual oil company access to their land. The well pad may no longer be used for grazing or cropping, and trucks and rigs need rights of way for tracks and lay-down areas. Accordingly, the farmer is paid a share of the profits that come from the well’s output. In Queensland, these pads are the single most valuable acreage on the farm based on cash proceeds per square meter.

Wells may have more than one owner, as in the case of joint ventures involving two or more oil and gas companies, and over their lifetime, wells may be sold wholly or in part to other parties. To add to the challenge, wells can be in operation for decades, and have to be handed down from one management team to the next. Of course, the land can be sold too.

Governments are also deeply involved in royalties. In Canada, the hydrocarbon resource is actually owned by the province, who in turn set out the provincial royalty calculations. In Australia, the states own the resources in much the same way as in Canada. In the US, the landowner owns the rights to whatever is extracted under their land. Internationally, governments will set the royalty rates for resources extracted in their territories.

You’d think the math to calculate the royalties owing to the various parties would be pretty straightforward, but it’s not. Sometimes governments want to stimulate development of specific resources in specific areas, and so the royalty rates may be reduced. Sometimes certain costs are deducted from the royalty calculations in an accelerated fashion to stimulate activity. Sometimes specific rules are in effect for certain timeframes. When governments change, they often can’t resist tinkering with the royalty rates to extract more revenue from the industry. Finally, it’s a trick to anticipate all the different costs that could be incurred against the well and the rules for sharing those costs.

In any case, the parties to the well get together and agree how the revenues and costs are going to be divvied up amongst them and write up the sharing agreement in the form of a contract. The contract then becomes the basis for determining who is owed what. In international settings, the contract might be called a Production Sharing Agreement.

Oil and gas companies must then employ an army of back office production and royalty accountants, lawyers and systems professionals to figure out the math each month. This system has been in place forever and its sole innovation in 20 years has been the introduction of spreadsheets to do the calculations.

Math Challenged

As you can well imagine, the system is prone to error. There are hundreds of thousands of wells, paper contracts, and corresponding excel spreadsheets that need to be maintained. The parties try to maintain their own separate records, the effort is largely manual, the calculations are monthly, and the calculation formulas change frequently. Larger players might have their own dedicated systems, but that’s unaffordable by the smaller guys.

The formulae in the spreadsheet are frequently wrong, or out of step with the contract, or no longer current with the prevailing rates and rules. The well may have changed ownership.

The system is easily manipulated. Royalty holders suspect that well operators are motivated to exaggerate the costs, and minimise the value of the production, which distorts the royalty value. Operators may delay the royalty payments to manage cash flow.

Suffice to say the industry is rife with disputes, which incurs needless legal fees, the occasional court challenge, payment delays and ever more complex agreements.

Unfortunately, the system is highly resistant to change. In early 2017, I met with the Senior Vice President of a leading Canadian oil and gas outfit to discuss the impacts of commodity pricing during the downturn, by that time well into its 36th month. He was quite proud to point out that while his company as a whole was well into its third restructuring, there had been no impact at all on his finance and accounting teams.

Digital to the rescue

There are a variety of ways to apply digital to this problem – a huge volume of English-language contracts that need specialists to interpret them correctly, and low levels of trust between counter-parties 

Natural language processing

The  royalty agreements must first be “read” and interpreted properly, to extract the clauses that are the basis of the royalty calculation. This is a machine learning problem and has been solved a number of times already – the combination of Adlib and IBM Watson is one solution in use at Swiss Re to ingest and interpret legal contracts. Here’s an example from my own firm involving tax rules in Europe. The key is the availability of the technical specialists that supervise the machine as it learns (ie, the royalty expertise).


Robotic process automation takes over, mimics the keystrokes of a human, and builds the computer instructions for a smart contract on a distributed ledger, based on the interpreted agreements.


The smart contract is finally posted to DLT for automatic execution once key conditions are met – presumably the availability of the data that is used in the calculation of royalties (revenues, costs, rates).

Is this worth fixing?

It’s easy to see why oil and gas companies, landowners, government and regulators might want to modernise how royalties are handled.

Eliminate disputes

Disputes have been long seen as a cost of doing business, but in the downturn, that cost looms larger on a relative basis.

Acceleration of cash flow

No payments happen while a dispute is outstanding, so elimination of disputes accelerates cash flows for the royalty holder. We’re talking a lot of cash. CFOs ought to like that.

Cost reduction

There are two kinds of cost savings. Fewer disputes means lower administration costs for both the operator of the well, and the royalty holder. Both parties gain. The second cost saving comes from reducing the monthly effort to calculate the royalty, which is automated away. Reduction of both costs means the royalty value should go up, which should please governments and royalty holders. And CFOs.

More nimble business

When royalty rates change, smart contracts will be much easier and faster to adapt than excel spreadsheets. We should see a reduction in the uncertainty and volatility in royalty values. Investors should like that.

The implications

What does this use case of machine learning coupled with the use of DLT smart contracts mean for oil and gas?

Exploiting other areas

Royalty calculations are just the first of what could be a new wave of artificial intelligence applied to oil and gas. There are many other types of paper contractual agreements in oil and gas that could also benefit from machine learning and smart contracts.

  • Services agreements
  • Land and lease agreements
  • Supply chain contracts
  • Procurement and purchasing contracts
  • Joint venture agreements

Oil and gas companies can take advantage of these capabilities as they up their game in artificial intelligence and DLT.

Rethinking ERP deployments

There are a number of large scale ERP deployments and upgrades presently underway in the oil and gas industry, and many more to come. This use case demonstrates how some ERP processes could be quite different using digital (smart contracts on DLT may not require invoicing, receivables and payables processes). ERP deployments could capture significant new value with digital enhancements.

Impacting human capital

This royalty example points out yet again how digital technologies are impacting what would be considered high end, privileged jobs (legal, accounting, dispute, land). The future of work in oil and gas companies is going to look very different.

So What?

Here’s my advice to oil and gas executives.

  • Executive sponsors need to be challenging ERP teams about how DLT and blockchain technology coupled with machine learning could be applied to business processes. There is too much back office cost at stake.
  • Human capital leaders in oil and gas need to get ahead of these technology developments to help cushion the blow to headcount.
  • CFOs, in particular, need to prepare their organizations to embrace these new technologies. DLT and machine learning will come quickly to the industry, since the industry loves to follow a leader. The future of finance should feature a lot more smart machines.

  • Kelly Derlago
    Posted at 14:02h, 05 March Reply

    Great article Geoffrey! Outside of Oil and Gas who is leading the way implementing digital contracts as you describe?

    • geoffrey cann
      Posted at 09:38h, 07 March Reply

      Kelly – I don’t know who the market leaders are. That’s very good question.

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