Deepwater offshore drilling industry has the capability to drill wells by maintaining a ship position within 1m of designated location for months and drill in water depths upto 12,000ft and drilling depth of 35,000ft at upto 20,000psi pressure. One would imagine that with such capabilities the digital optimization would be ingrained in this field. Unfortunately, that’s not the case at all.
Revolutionary improvements have been made in downhole drilling technology by means of enabling remote real time decisions to be taken based on data of the formation being drilled at significant depths at very high temperature and pressures. These tools and services are provided by companies like Schlumberger, Halliburton etc. However, another significant cost in exploration is associated with rigs that are owned and operated by drilling contractors on contract basis with the oil companies. The equipment on these rigs have become quite advanced but the operating models of the drilling contractors is still very archaic.
Reliability of operations I will argue is the most important factor in this industry. Reliability in terms of both safe operations and uptime. So far philosophy to achieve reliability has been around mainly having experienced crews onboard. During the climbing oil price post Macondo in the period of 2010 to 2014, the industry thus spent most of the upside on retaining experienced people who were in high demand increasing the overall cost structure of the industry to significantly high levels (to the order of 60% of operating cost in a high capital intensive industry). The crash in oil prices thus made the cost structure unsustainable.
This has brought in the urgent need for a low cost reliable solution which the industry sees in digital transformation.
Three components are required to gain operational efficiencies by digitalization:
- Data collection
- Data analysis
- Data being used to drive decisions
Data collection is relatively straight forward in terms of hardware installation – though the initial capex to achieve this is a huge deterrent in this oil price market. The latter two is where I see main problems with this industry. First, most drilling contractor organizations have become very decentralized without much process oriented technical capabilities at unit level. This makes data analysis a challenge. Secondly, regulatory environment has also pushed to a very conservative periodic philosophy for maintenance and operation compared to a condition based philosophy.
One of the ways the industry is going about resolving the issue is with equipment manufacturers extending service contracts linked to equipment performance instead of equipment sale (similar to aircraft engines by GE in airline industry). Earlier this year one of the offshore drilling contractor signed a similar contract with GE for BOP maintenance (BOP is among the most critical equipment used on drilling rigs – last line of defense for preventing blow out). This model makes sense for multiple reasons:
- If the manufacturing data can be supplemented with operational data –the iterative improvements in equipment can be achieved much faster resulting in a more reliable equipment
- The incentives of the drilling contractor and equipment manufacturer are aligned
- Regulatory considerations post Macondo makes reliance on OEM more favorable for compliance
However, from a drilling contractor’s point of view there are a few downsides to this strategy of digitalization. Near monopoly of major equipment manufacturers is an issue already resulting in very high costs for drilling contractors. Increasing this reliance by outsourcing maintenance and hence related data will possibly result in entire value to be captured by equipment manufacturers .
It is thus important for the drilling contractors to enable this competency inhouse in collaboration with equipment manufacturers.
Operational changes have to be made to achieve this primarily on the latter two aspects of digitalization in addition to initial hardware capex investment. A change from current decentralized structures to a level of centralization required to enable consistent operations and subsequent data analysis. Internal ERPs have to be made more interlinked and dynamic to capture and link various data sets. A shift in mindset is required to make decisions based on data analysis instead of finding data to support experienced instinct.
I think getting the right talent to make this change faster is key. Cross industry learning is extremely limited in this industry and this needs to change for efficient and economical change process.
Experienced workforce is definitely required to phase this shift in to provide initial validation of data derived decisions. However, investment in digital transformation will prepare the industry much better for the next phase when the market for oil and gas industry improves and sustainable efficiencies can be developed instead of the cyclic cost-cutting/cost inflation responses to this cyclical industry.
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