Mozambique LNG: Can Chevron create more value?

Chevron Mozambique Technologies

It would be unwise to under-estimate the complexity of creating a new LNG province, with a 50MTpa prize on the table in Mozambique. After the first two trains are in motion, the longer-term opportunity is potentially “another Qatar”. But only if Mozambique can compete for capital with US greenfields and brownfield expansions.

Hence we have reviewed 200 of Chevron’s patents from 2018. The company’s ability to develop a new, deep-water LNG province is notable. Ten examples are tabulated below.

It was interesting how many of the patents were filed in Australia and may have derived from learnings at Gorgon and Wheatstone.

For a primer on different LNG process technologies, please see our data-file (here).

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EOG’s Completions: Plugged-In?

EOG Sensor Plugs

EOG has patented a system to deploy pressure and temperature sensors in its frac plugs, which are then retrieved at the surface, providing low cost data on each frac stage. The data can be used to improve subsequent frac stages. We model the economic uplifts at +$1M NPV and +5% IRR per well (at $50 oil).

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EOG screens among the leaders in shale technology, based on the patents and technical papers we have reviewed so far. However, the company is secretive over its intellectual property, notoriously banning camera-phones from its well-sites and publishing fewer technical papers relative to its peers.

EOG Sensor Plugs

However, last year EOG filed a patent for one of its data-methodologies, which we believe is being applied in its operations in Texas.

Specifically, EOG is housing “sensor pods” in its frac plugs. Each of these pods can record 50-100k data points, logging temperature and pressure during a frac stage. Later, the frac plugs are released, and retrieved back at the surface, where their data can be downloaded.

This methodology allows EOG to measure actual frac pressures down-hole, close to the perforations, for each, individual frac stage. The readings are likely to be much more accurate than the inferences from the surface. Downhole temperatures can also be measured.

Why is this useful?

First, the data can be used to enhance EOG’s modelling of the fracture network. In turn, this can be used to infer mechanical properties of the formation, and optimise future frac stages: tailoring perforation geometry, injection rates, sand concentrations, fluid viscosity and chemicals compositions.

Moreover, the data can be used to detect problems. If a frac stage has not been properly isolated, then pressure will not build up as much on either side of the frac plug. If a well is unexpectedly flowing(/not flowing), then downhole fluids will be warmer (/cooler). In another design, the sensors can be placed in neighbouring wells to detect frac hits.

If all of these factors can increase well productivity by c10%, then we estimate the NPV uplift at $1M NPV or +5% IRR per well. The technology breaks even if it can uplift EURs by c2.5%. These numbers vary based on the oil price (chart below, model here).

Wouldn’t fibre be better?

We have seen other operators making enormous strides deploying down-hole fibre-optics, to monitor pressure and temperature, meter-by-meter, in real-time across a 20,000ft well. This would offer more granular data, immediately. I.e., you would not need to wait until the sensor pods are retrieved at the surface to download their data.

However, we do not believe the cutting edge of fibre is currently practical for common usage in the shale patch: running the complete works of fiber-optics across an offshore well can surpass $1M. As we have learned from other patent-filings, retrievable plugs can be run “at a fraction of the cost associated with a tethered downhole sensor”. Our numbers above assumed $0.4M incremental costs for deploying EOG’s sensors across a 40-stage stimulation.

Another leading example of big-data

As we have highlighted in ‘Winner Takes All‘, shale is increasingly a ‘tech’ industry, harnessing advanced modelling or data-based optimisation in 60% of the 300 technical papers we reviewed from 2018 (chart below). So here is a cutting edge example from EOG.

References

TSE Shale Database

Bustos, O, Raizada, S., James, C. et al (2018). Completion and Productions Apparatus and Methods Employing Pressure and/or Temperature Tracers. US Patent No 2018/0252091 A1

Naldrett,  G., Cerrahoglu,  C. and Vahue, M. (2018). Production Monitoring Using Next Generation Distributed Sensing Systems. Petrophysics. Volume 59.

Deffenbaugh, M., Ham, G. D., & Alvarez, J., O., et al (2016). Method And Device For Obtaining Measurements Of Downhole Properties In A Subterranean Well. Saudi Aramco Patent US2016320769 

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Is gas a competitive truck-fuel?

is gas a competitive truck-fuel

We have assessed whether gas is a competitive trucking fuel, comparing LNG and CNG head-to-head against diesel, across 35 different metrics (from the environmental to the economic). Total costs per km are still 10-30% higher for natural gas, even based on $3/mcf Henry Hub, which is 5x cheaper than US diesel. The data-file can be downloaded here.

The challenges are logistical. Based on real-world data, we think maintenance costs will be 20-100% higher for gas trucks (below). Gas-fired spark plugs need replacing every 60,000 miles. Re-fuelling LNG trucks requires extra safety equipment.

is gas a competitive truck-fuel

Specially designed service stations also elevate fuel-retail costs by $6-10/mcf. Particularly for LNG, a service station effectively ends up being a โ‚ฌ1M regasification plant (or around $250/tpa, costs below).

is gas a competitive truck-fuel

We remain constructive on the ascent of gas (below), but road vehicles may not be the best option.

is gas a competitive truck-fuel

To flex our input assumptions, please download our data-model, comparing LNG, CNG and other trucking fuels across 35 different metrics .

Machine Learning on Permian Seismic?

machine learning on permian seismic

Pioneer Natural Resources is improving the accuracy of its Midland basin depth-models by up to 40%, using a machine-learning algorithm to re-calibrate its seismic from well logs. Faster drilling and better production rates should follow.

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Pioneer Natural Resources has patented a new methodology in 2018, to calibrate its seismic images in the Permian, with reference to its well-logs. Ordinarily this task would be challenging and time-intensive. But the new algorithm employs machine-learning. This places it at the cutting edge of Permian data-science, where just 2% of technical papers have used ML in the past year (chart below).

machine learning on permian seismic

Specifically, a multi-layer neural network model iteratively improves the estimates of key seismic parameters from the log data (e.g., impedance, sonic velocity, Youngโ€™s modulus, Poissonโ€™s ratio) (chart below). This algorithm improves the vertical accuracy of seismic interpretations by up to 40%.

The neural network creates different inversion volume estimates (208) from the well logs (202) and their attributes (204)

Improved well-placement and geo-steering. The patent cites how โ€œreflectors that were previously unmappable on conventional seismic data can be mapped so horizontal wells can be more accurately placedโ€. This will be used to target wells into larger-capacity reservoirs and to inform well completion parameters.

Improved drilling-times. The company also cited a need to avoid drilling through carbonate debris flows in the Midland basin. They are excessively hard, damage drill-bits and lead to costly โ€˜tripsโ€™. Instead, it is intended to use the better-calibrated seismic to steer well-paths through brittle organic facies. Thus, we expect the innovation to lower costs and improve well-economics

Pioneer screens as one of the top quartile operators, across all the technologies we have diligenced so far (chart below). Although, please note, we are still “early” in our project to categorize who has the best technologies in oil and gas.

If you would like to read our latest deep-dive note on shale-technology it is linked here. The full database, covering all 300 technical papers is available here.

Patent Source: Meek, R., (2018). High Resolution Seismic Data Derived from Pre-Stack Inversion and Machine Learning. Pioneer Natural Resources USA, patent WO2018201114

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Who else wants more shale?

who else wants more shale

The Majors’ deepening interest in shale was illustrated by Chevron’s $50bn acquisition of Anadarko. Consolidating in the Permian fits our ‘Winner Takes All‘ thesis.

But who else wants more shale in their portfolio? This is not to speculate on M&A, but simply looking at the companies’ research activity last year…

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Hence the chart below summarises 300 technical papers into shale, published across a representative sample of companies in 2018.

Shell has been the most active shale researcher by a wide margin — half in the Permian, half internationally.

who else wants more shale

The companies who are not on this list may also be more interested in corporate M&A. This is a technology industry. And if you don’t have your own technology, you will need to buy it…

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If you would like to read our latest deep-dive note on shale-technology it is linked here. The full database, covering all 300 technical papers is available here.

Copyright: Thunder Said Energy, 2019-2024.