The next installment our Q&A series with geomechanics expert, Stanford professor, and Petro.ai Technical Advisor Dr. Mark Zoback touches on how a better understanding of geomechanics can lead to completion designs that improve well performance.
Loss of contact with the borehole. Cave-ins. Sub-optimal mud types. Recording errors. Unit conflicts. Equipment failure. A number of things can cause a Well Log to have bad hole readings. Perhaps the Caliper log indicates a series of unreliable borehole sections, and an expert has flagged them. Perhaps the expert has run an outlier detection algorithm to identify aberrant well log readings.
What next? What to do? Re-logging the well is often cost prohibitive.
We’ve built a Synthetic Well Log tool in Spotfire that uses machine learning to help replace those bad hole values with more accurate ones.
Our tool uses the theory behind academic studies (e.g. An Artificially Intelligent Technique to Generate Synthetic Geomechanical Well Logs for the Bakken Formation, Synthetic well logs generation via Recurrent Neural Networks, Generating Synthetic Well Logs by Artificial Neural Networks (ANN) Using MISO-ARMAX Model in Cupiagua Field) and supports several machine learning algorithms (Random Forest, Gradient Boosting, and Support Vector Machines). The algorithms ingest the curves which do not have data integrity issues in order to predict more accurate values of the missing or faulty curve.
Jumping into the machine learning arena might feel daunting, so we developed this tool to help Geo experts with the process. Better yet, our tool doesn’t just reach into a black box and hand back a reconstructed well log. The Synthetic Well Log tool:
- Works with the Geo expert to build an imputation model
- Lets that person examine modeling validation metrics
- Displays the predictions and reconstructed logs next to the original for a sanity check
- Exports the chosen reconstructed model
Our tool puts reconstructed curves right next to the originals so they pass the expert’s eye test as well as the modeling diagnostics.
Petro.ai 4 is here and it’s a big one! Major updates have been added throughout, including an all new web application supporting decline curve analysis as well as machine learning. Users familiar with our previous decline curve tools will recognize some of the intuitive features but now you can batch decline wells and create type curves all in the web. Decline models can be easily moved between ARIES, Spotfire, Excel, and Petro.ai; allowing users to easily compliment existing workflows.
In your browser you can view your wells and filter by any number of parameters to quickly navigate to wells of interest.
Figure 1: Load in public and/or private data, view your wells on an interactive map and easily filter down to your wells of interest.
Type curves are easier than ever! You can dynamically select wells and update oil, gas, and water type curves. These type curves can be saved back to Petro.ai for the same sort of manual tweaking as a single well decline. They are also version controlled and can be recalled and overlaid on new models.
Figure 2: Dynamically generate probabilistic type curves directly from your selection.
Like our previous forecasting tools, you can configure your default decline parameters. Now you can save your defaults or have different set of default parameters for different basins or situations that can be quickly recalled. Flags can be configured to give a quick overview of the quality of fit to enable management by exception.
Figure 3: Configure your decline model and parameters; as well as setup flags for management by exception.
The intuitive user interface puts control at your fingertips – switch to a rate-cum view or toggle on/off individual fluid streams.
Figure 4: Easily switch how you view the declines.
This release of Petro.ai introduces a new social collaboration framework; a first for our industry. You can comment on any data point or model. These comments facilitate collaboration and capture key insights right next to the relevant data. You can also send notifications using @ or create searchable keywords with #.
Figure 5: Comment on any data point, use @notifications and #keywords.
It’s now easier than ever to see how changes to a single decline parameter effect a wells productivity.
Figure 6: Update the auto-forecast and instantly see how the changes effect remaining reserves and EUR.
The production forecasting app is great for asset teams, A&D teams, and even reserves teams. With full audit traceability and a built in approvals workflow, decline models are version controlled and can be rigorously managed.
Figure 7: Decline models are automatically version controlled and tracked for auditability. Petro.ai also supports approval workflows.
Oil and Gas companies are aware of their environmental responsibilities, the financial re-directing of governmental decarbonization initiatives, and the growing perception towards climate change. Responding to these concerns, supermajors are investing in battery innovation and renewable energy resources. The acceptance by the O&G community that climate change must be addressed has increased consumer trust as indicated by the steady climb in the 2018 Edelman Trust Barometer since 2014. (https://www.forbes.com/sites/uhenergy/2018/04/05/prices-are-up-but-challenges-remain-for-oil-and-gas-companies/#2e14481b213d)
But there’s more to the environmental question than adherence to regulations and alternative fuels. O&G companies realize that resource productivity—getting the most out of a well, isn’t just economically important; it’s a vital part of environmental stewardship.
Renewable resources still need time to mature. Under the Obama administration, Mark Zoback, professor of Geophysics at Stanford University and Technical Advisor to Petro.ai, served on a panel to address the environmental impact of shale gas production. In a Stanford Report article, he noted that “the global energy system is so huge that even if we move as quickly as possible to develop renewable energy sources such as wind, water and solar, we will need to continue using fossil fuels until mid-century.” (https://news.stanford.edu/news/2011/august/zoback-fracking-qanda-083011.html)
With that stretch of time looming ahead of us, O&G companies are faced with the difficult tasks of (1) figuring out how to effectively fracture the low permeability shale strata to release cleaner burning natural gas and (2) how to recover oil that’s being left in the ground.
As a bridge to our green energy future, natural gas provides a transition alternative that produces fewer pollutants than either coal or oil. Globally, the amount of shale gas is enormous, enough to provide a 100-year buffer at current consumption to the time when those alternative energy sources are ready. Particularly for the creation of electrical energy, natural gas is replacing coal and reducing the carbon footprint by more than 20% since 2006. (See also https://www.youtube.com/watch?v=ChNeFTNEO9c.)
Then there’s that tough to extract oil still lying untouched in fields across the world. According to experts, that residual represents two-thirds of the oil in known fields. (https://www.technologyreview.com/s/410160/oil-left-in-the-ground/)
There are many reasons for this large untouched percentage including the rate of extraction in a well, poor geology, well spacing, well construction, or poor hydraulic fracturing methodology.
Both natural gas extraction and left behind oil can be addressed. The data is there for understanding the oil and gas field, but the complexity of the factors to optimize shale extraction requires a multi-faceted, multi-layered approach.
To answer this need, Petro.ai collaborates with Dr. Mark Zoback to embed geomechanics in a platform that bundles data conditioning with machine learning and visualizations to enable analytics at the well, section, and asset level. Petro.ai enables O&G companies to determine the individual elements of a well plan that have the greatest impact on production, boosting the amount of oil extracted from each well and optimizing the correct approach for unconventional drilling.
By increasing the recovery from a well, O&G companies answer the call to environmental stewardship, giving us the buffer we need to move towards a world of alternative energy sources.
Petro.ai is proud to announce that it joins industry leaders Schlumberger, Chevron, Microsoft, Shell, and others in membership in the Open Subsurface Data Universe™ Forum. The OSDU is developing a cloud-native data platform for the oil and gas industry, which will reduce silos and put data at the center of the subsurface community.
Membership in the OSDU Forum gives Petro.ai a seat at the table in developing the latest standards in petrotechnical data access and integration. Leveraging the OSDU data platform, Petro.ai accelerates the oil and gas digital transformation: empowering asset teams to organize, share, and interact with data like never before.
Learn more about the OSDU here.
Like many in our industry, the team here at Petro.ai keeps a close eye on oil prices, rig count, and analyst reports to stay in tune with what’s happening. Richard Gaut, our CFO, and I were discussing the recent trends in the rig count which led us to dive into the data. Essentially, we were curious as to how different types of E&P’s are adjusting their activity in the current market. There’s been a lot of news recently on how the super-majors are now finally up to speed in unconventionals and that smaller operators won’t be able to compete.
Figure 1: North American Rig Count (from BakerHughes)
The figure above shows the North American rig count from BakerHuges and we can see the steady recovery from mid-2016 through 2018, followed by another decline in 2019. But has this drop been evenly distributed among operators? TPH provides a more detailed breakdown of the rig count, segmenting the rigs by operator. I put the operators into one of three buckets:
- Large-cap and integrated E&P’s
- Mid-cap and small-cap publicly traded E&P’s
- Privately held E&P’s
The figure below shows the breakdown between these three groups overlaid with the total rig count. You can see the mid and small caps in yellow are a shrinking segment. Figure 3 shows these groups as percentages and makes the divergence between the groups extremely visible.
Figure 2: Rig count segmented by operator type
Figure 3: Percentage breakdown of rigs by operator type
Since the recovery in 2016, privately held companies have taken on a larger share of the rigs and that trend continues through the recent downturn in 2019. This is likely because they are tasked by their financial backers to deploy capital and have no choice but to keep drilling. The large-caps and integrated oil companies have been staying constant or growing slightly since mid-2016 as has been reported. These operators have deep pockets and can offset losses in unconventionals with profits made elsewhere – at least until they become profitable. The story is very different for the small and mid-caps. These operators have experienced the sharpest drop in activity as they are forced by investors to live with in their cash flows.
The data used in this analysis were pulled at the end of September. We typically see a slowdown in activity in Q4 and recent news shows that this slowdown might be worse than normal. It’s likely the divergence we see will only continue through the end of the year.
Next, I split out the rig count by basin and found some interesting trends there which I’ll elaborate on in a second blog post.