Epistemic Challenges for Subsurface Engineering, Part I: The Persistence of False Beliefs


To optimize economic performance in shale, engineers and geoscientists optimize key design variables: well spacing, cluster spacing, fluid volume, proppant volume, fracture sequencing, and more. They use a variety of tools: computational modeling, statistical analysis, laboratory experiments, field-scale data collection, and trial and error. Thanks to these efforts, there have been tremendous improvements in well and fracture designs, and there have been major advances in our understanding of the subsurface.

It is important to recognize that each of the above approaches have limitations. Physics-based analyses face the difficulty that processes are happening deep in the subsurface and are difficult to observe directly. Laboratory analyses face the difficulties of reproducing field conditions and extrapolating results from the laboratory to the field scale. Data-driven analyses face the problem that statistical analyses are ‘observational’ and so are vulnerable to the effect of variables not included in the analysis, complex nonlinear relationships, and ‘false discoveries’ resulting from the post hoc nature of the analysis (Benjamini and Hochberg, 1995; Prasad and Cifu, 2011).

The shale industry sometimes struggles to deal effectively with these challenges. Engineers may jump to conclusions before a hypothesis is firmly proven. Beliefs become entrenched and subsequently are difficult to dislodge. The oil and gas industry is not alone in this regard. These problems are encountered in many fields of science. Akerlof and Michaillat (2018) review how, in fields of science where proof is relatively difficult to achieve, group dynamics cause false beliefs to endure. More troubling, they provide historic examples of false beliefs persisting even after they have been conclusively disproven. This happens in fields that lack a strong commitment to rigorous standards of proof.

In the short term, one solution is to gather better data. The industry shale is, in fact, gathering outstanding subsurface data. In the past 5-7 years, extraordinary subsurface characterization has transformed the industry’s understanding of hydraulic fracturing and production in shale.

In the medium to long term, the solution is to recognize the problem, and then to build practices and culture around improved standards of proof. This may require a rethinking of our approach towards subsurface engineering in shale.

In Part 1 of this blog post, I provide examples of false beliefs that became widely entrenched across the industry. I discuss how and why this may happen.

In Part 2, I discuss solutions – how can we reconsider our approach to subsurface engineering in order to evaluate claims of truth and drive long-term value? I propose a hypothesis-driven approach, in which field testing is placed at the center of our efforts to assess the truth and improve over time. In a hypothesis-driven approach, physics-based and data-driven approaches are used as hypothesis-generating activities that motivate and prioritize hypothesis-testing through field operations. Statistical lookbacks use field data, but should usually be considered only hypothesis-generating, and not hypothesis-testing, because they use observational study design and because they are performed post hoc.

Field data is considered paramount, but combined physics-based and data-driven analyses are critical to the hypothesis-driven workflow. We need to prioritize which ideas to test, and to decide which ideas are not worth testing. Prioritization is performed by identifying the key design decisions that affect our objectives (such as return on investment), and then evaluating opportunities for improvement, balancing risk/reward.

The hypothesis-driven approach emphasizes the coordination of field operations to enable clean well-to-well production comparisons and the design of field data collection to enable strongly supported conclusions. Field testing need not increase the cost of field operations if it is done through intentional and thoughtful planning.


  1. Introduction
  2. Examples of mistaken beliefs
  3. What’s going wrong?
  4. Recap


In a recent blog post, I outlined how companies use field tests, modeling, statistical analysis, and laboratory studies to improve over time (Abivin et al., 2020). Information is synthesized as part of an iterative process of continuous improvement. In this post, I discuss what happens when the process of continuous improvement runs into trouble.

In uncertain environments (like subsurface shale), there is a tendency toward overconfidence. We need to act, and in doing so convince ourselves that we are making the right decision. Sometimes we hire experts who ‘confidently confirm’ our beliefs. This is symptomatic of a phenomenon called confirmation bias, where we tend to ignore new data and outcomes that contradict our initial beliefs.

After committing to strong claims, we may have difficulty changing course when it becomes apparent that they are not consistent with observations. This can cause false beliefs to persist for years, long after they have been falsified by field data.

Fortunately, most operators understand this dynamic and take steps to mitigate it. They create high-level roles within their organizations where they empower people to think critically and synthesize information. Engineers in these integration roles try to figure out ‘what is going on and why?’ They critically evaluate different data sources and workflows and decide which to weight more or less. They actively seek diverse perspectives and are ready to change their mind if justified.

Also, in recent years, the quality of field data collection has been exceptional. Operators have gathered high-quality, detailed data that provide excellent calibration for our subsurface models. Thanks to their efforts, we have never been in a better position to do physics-based modeling in shale.

Based on conversations with colleagues, I believe that frustration with these problems is widespread. Nevertheless, they are seldom discussed publicly. The topic is difficult to talk about because it can come across as being overly negative or critical.

However, I am a strong believer in the value of a growth mindset and in the importance of continuous improvement. If we can openly and nonjudgmentally discuss challenges, then we can take steps to resolve them.

In Part 1 of this blog post, I provide specific examples of persistent, widespread beliefs that I believe to be either weakly supported or very likely false. In Part 2 of this blog post, I discuss strategies for discovering the truth more reliably and more efficiently. Improvements could drive tremendous value in both the short and long-term.

Examples of mistaken beliefs

This section provides examples of (what I believe to be) mistaken beliefs that have become widespread in subsurface engineering.

Permeability, effective fracture lengths, and RTA

As discussed in a recent post, Barree et al. (2009) recommends an equation for estimating permeability from diagnostic fracture injection tests (DFITs) that is systematically too high. In an upcoming paper, we test the method on 52 field DFITs, and find that it overestimates permeability by 10-1000x or more (McClure et al., 2021).

If permeability is overestimated, rate-transient analysis (RTA) history matches are still possible if you underestimate fracture length. Barree et al. (2015) use their permeability estimates with RTA to infer effective fracture length of tens of feet, and then apply these results directly in economic optimization of well spacing and frac design. These problems may be compounded by the application of single phase RTA solutions to production data that is significantly impacted by multiphase flow.

Their workflow is embodied into a series of software modules that are sold commercially and are widely available across the industry. The method is recommended in the recent SPE hydraulic fracturing textbook (Chapter 13 from Miskimins, 2019).

The results from Barree et al. (2015) are inconsistent with field data. We know that effective fracture lengths are not tens of feet because interference tests between wells demonstrate connections that traverse many 100s of feet over a short period of time (Rucker et al., 2016; Rafiee and Grover, 2017; Liang et al., 2017; Cipolla et al., 2018; Li et al., 2018; Raterman et al., 2019; Fowler et al., 2020).

In Fowler et al. (2019), we stepped through a well spacing and cluster spacing optimization in the Utica shale. We performed the optimization twice: first using a low permeability (believed to be correct), and then using 100x higher permeability (similar to what would be yielded from the Barree et al. (2009) equation). The low permeability estimate is confirmed by data, which shows that outer wells outperform inner wells within weeks of going on production. We show that overestimating permeability and underestimating effective frac length will lead an operator to use excessively tight well spacing and excessively wide cluster spacing. There is a large negative impact on economic performance.

There are other problems with the Barree et al. (2009) workflow. For example, their recommended procedure for stress estimation is contradicted by direct in-situ observations. Mathematical solutions to the governing equations confirm that it is inaccurate and explain why (McClure et al., 2016; 2019; 2021). Nevertheless, the method remains in wide use (though, its adoption has declined in recent years).

Deep closed-loop geothermal

As discussed in a recent blog post, there has recently been a lot of enthusiasm for deep closed-loop geothermal. This enthusiasm appears to have arisen from inaccurate calculations of their potential for energy production (van Oort et al., 2021). Properly applied, physics-based solutions predict that these designs yield very low energy output, ~100x lower than production from a conventional geothermal well, even though the systems would cost more than a conventional geothermal well. Schemes placing closed loop coaxial heat exchangers inside vertical wells also yield very low energy production (Wang et al., 2010).

But at this moment, there is a considerable momentum behind these concepts, and companies are raising substantial funds to implement them.

In contrast to the above geothermal concepts, physics-based modeling does suggest an exciting alternative set of technologies for next-generation geothermal systems . Therefore, while I am pessimistic about conduction-based closed-loop designs, I remain optimistic about the overall prospects for next-generation geothermal concepts.

Complex fracture networks

In the early days of shale, engineers hypothesized that they may be creating zig-zagging fracture networks that are sometimes called ‘complex fracture networks’ (Fisher et al., 2002). One of the major service companies built their flagship fracture modeling code on this concept (Weng et al., 2011), and it has been the subject of significant software development in the private sector and academia (including a simulator that I developed prior to starting ResFrac). It was a reasonable hypothesis, worth investigating. But, because of extraordinary efforts by operators in recent years, we now know that the hypothesis is not correct in shale, at least based on the overwhelming preponderance of evidence available to date (Raterman et al., 2017, 2019; Gale et al., 2018; Ugueto et al., 2019a; 2019b; Shahri et al., 2021; Wu et al., 2021; Liu et al., 2021).

If you look back on my work from 10 years ago, I accepted the plausibility of these models and used them, but I was concerned that they were not well-validated at field scale. I wrote papers on how we might try to diagnose and differentiate stimulation mechanisms from field data (Chapter 3 from McClure, 2012; McClure and Horne, 2014a; McClure and Horne, 2014b; McClure et al., 2016). I was concerned that if you use a modeling code based on the wrong physical assumptions, then that undermines everything that comes out of the model.

Fortunately, the industry has gathered several highly informative datasets, such as core-through studies HFTS1, HFTS2, and the COP project in the Eagle Ford, and developed technologies such as offset fiber (Raterman et al., 2017, 2019; Gale et al., 2018; Ugueto et al., 2019a; 2019b; Shahri et al., 2021; Wu et al., 2021; Liu et al., 2021). These datasets provide clear, unambiguous observations on what fractures look like in the subsurface. They are not zig-zagging complex fracture networks dominated by fracture termination and branching. They form individual strands or swarms of subparallel hydraulic fractures in narrow bands oriented consistently in the direction of SHmax. This observation has now been reproduced across many shale plays, by many operators. The observations directly contradict the complex fracture network conceptual model.

Unlike my two prior examples, which involved the application of flawed physics-based models, complex fracture network models are typically grounded in sound physical assumptions and accurate mathematical solutions. They are based on real observations and are representative of real fracturing processes. They simulate mechanisms that have been observed from in-situ tests (albeit, in shallow, low-stress formations not necessarily analogous to commercial shale plays).

Nevertheless, applying them to the design of field-scale hydraulic fracturing treatments in shale has always required a lot of assumptions. We now know that some of those assumptions were not entirely robust, and that these models do not appear to represent what is happening in the subsurface in most shales.

This is a complex issue, and I think that we still may find complex fracture network models useful for certain applications. Still, it is now clear that people have been too quick to jump to conclusions, and too slow to seek hard validation. The concept has been confidently applied in many situations where it was not applicable.

Complex fracture network modeling is used less widely in the industry than it used to be. But there are still some major companies primarily using these tools, and they remain a popular topic for code development in academia. Indeed, proponents will still tell you that they are ‘state of the art,’ and insist that they are the only way to model fracturing in shale.

In the past 5-6 years, companies have dramatically improved well performance by tightening cluster spacing, using limited entry, and almost entirely discarding swellable packer uncemented completions. I suspect that this transition was delayed by industry preoccupation with complex fracture networks. In years past, engineers argued that uncemented completions were desirable to avoid cementing off the natural fractures and because fractures branch out into wide fairways. But now, we know that in most shales, they do not typically branch out into wide fairways, and that open hole completions lead to excessively wide hydraulic fracture spacing and consequently, lower recovery. In years past, engineers argued that we could use wide cluster spacing with cemented plug and perf completions because, again, fractures branched out into wide fairways. We now know that we need to use relatively tight cluster spacing to force fractures to form at tight spacing.

It is fantastic that the industry has continued innovating and figuring things out. But it is natural to wonder if the industry had collectively been more cautious about embracing these complex fracture network concepts, would it have iterated to modern best practices years earlier?

Stimulation mechanism in geothermal

Engineers in geothermal very often assume that natural fractures play a dominant role in hydraulic stimulation, and that stimulation causes the formation of well-connected fracture networks in broad fairways of stimulation (see literature review from McClure et al., 2014b). These assumptions have mixed support from field data. In some cases, they seem well-founded, and in other cases, there are significant discrepancies (McClure et al., 2014b). Observations suggest that in many treatments, shear stimulation of natural fractures plays a limited role (McClure et al., 2014a, 2014b). Regardless of whether fluid flow localizes into natural fractures in the far-field, hydraulic stimulation tends to form a small number of dominant flow pathways, and does not form a dense, well-connected network (Baria et al., 2004; Tenma et al., 2008; Cornet and Morin, 1997; Parker, 1999).

Uncertainties about stimulation mechanisms in geothermal could be resolved with direct observation of far-field fracture geometry, supplemented by specialized injection testing (Chapter 3 from McClure, 2012; McClure and Horne, 2014a). However, because the shear stimulation paradigm has been rarely questioned, such data collection has not traditionally been prioritized.

The focus on natural fractures has affected how geothermal engineers have designed stimulation treatments. They have prioritized uncemented completions (to maximize contact with natural fractures), which makes mechanical isolation infeasible because of the unavailability of high-temperature open hole packers/plugs. They have mostly avoided experimentation with proppant (because shear stimulating natural fractures are expected to ‘self prop’), despite consistent reports of success in the rare cases when proppant has been used (see literature review from Shiozawa and McClure, 2014). Of course, in the petroleum industry, mechanical isolation and proppants have been used with tremendous success.

Despite these challenges, I am optimistic. There is now increasing interest in applying concepts from modern shale stimulation to geothermal. These approaches have the potential to create breakthrough improvements. There is also increasing recognition that newly forming, opening-mode fractures play a significant (or dominant) role in some projects, and there is increasing prioritization of the data collection required to diagnose stimulation mechanisms (Gugliemi et al., 2021).

Spaghetti fracs around child wells

For our parent/child industry study, I prepared a literature review of about 350 papers and synthesized them into a document. I found many modeling papers predicting that when you fracture a child well, the stress should rotate around the depleted parent well and cause the fractures to bend at sharp angles away from the parent well (Weng and Siebrits, 2007; Han et al., 2015; Marongiu-Porcu et al., 2015; Morales et al., 2016; Rezaei et al., 2017; Safari et al., 2017; Li et al., 2019; Sangnimnuan et al., 2019; Guo et al., 2019).

I did not find any papers documenting this occurring in field data. Nor have I heard about such evidence in conversations with operators. Conversely, there is a great deal of field evidence showing the opposite – that fractures propagate towards parent wells in straight lines with minimal turning. They may bend slightly as they propagate, but they do not appear to deviate at sharp angles. Fractures occasionally deviate due to leakoff into large-scale faults, but this is not the same thing as bending due to stress rotation.

The modeling studies are solving valid physical equations. The problem is that they almost uniformly assume that the maximum horizontal stress, SHmax, is very close to the minimum horizontal stress, Shmin. They usually assume the difference is 500 psi. Few of the papers explain this number; it is usually tabulated in a table of input parameters. But this input has a huge effect on the model results. If you assume that this horizontal stress anisotropy is low, then there is not much preferred directionality to fracture propagation. In contrast, if the stress anisotropy is high, the fractures tend to propagate with a consistent orientation.

In early ‘proof of concept’ modeling papers on parent/child interaction, the goal was to explore ‘what if’ scenarios and identify processes that deserve further study. But as time has passed and the number of these papers has grown, they have started to cite each other, leading to a circularity. Investigators seem to assume that because other papers are making similar assumptions, they must be correct. There hasn’t been enough questioning of the assumptions going into the models, or of their consistency with field data.

It is difficult to estimate SHmax, and so there is inherent uncertainty in the parameter. One method is to use earthquake focal mechanisms. Snee and Zoback (2020) provide a faulting mechanism map of North America. Many of the major shale plays in North America are in strike-slip faulting regimes (or close) – Marcellus, Utica, Montney, Duvernay, and SCOOP/STACK. Strike-slip faulting regimes imply large horizontal stress anisotropy – 1000s of psi. Even in the plays with normal faulting regimes, from equations provided in the papers, you can calculate that horizontal stress anisotropy is at least 1000-1500 psi, and often much more. These higher values of stress anisotropy are consistent with the empirical observation that sharp hydraulic fracture turning is rare (at most).

What’s going wrong?

Here is a working theory for what happens:

  • an operator has a problem and needs and quick and high-confidence answer,
  • someone proposes a solution and delivers what is requested – a quick and high-confidence answer,
  • there’s no such thing as free lunch, and so while the answer is provided quickly and confidently, it isn’t actually the right answer,
  • because of the expressed confidence, there is a tendency to commit to a particular workflow/solution
  • the commitment ups the odds of subsequent confirmation bias and that evidence of problems will be ignored,
  • because of confirmation bias, there is a risk of layering on even more complexity and rationalization; complexity has the supplementary effect of making the method seem ‘advanced,’
  • once the approach has taken hold, it is difficult to dislodge because the fact that it is widespread is taken as proof that it must be true.

This antipattern is possible because of the difficulty of proof in subsurface engineering. Everything we do is occurring deep in the ground. We usually cannot directly see what is happening in the reservoir (although, thanks to core-through studies, fiber, pressure monitoring wells, and other extraordinary technologies, sometimes we can). Also, the proprietary nature of data inhibits the dissemination of information. Researchers in academia usually have limited access to field data.

Another challenge is that it is surprisingly hard to assess the effect of design changes on well performance. Well-to-well performance is highly variable, and for operational and other practical reasons, it can be difficult to make clean apples-to-apples comparisons.

Technical issues are often complicated, and few have sufficient time to evaluate them deeply. For the few who do attempt detailed assessments of technical issues, their judgment is probably much more weighted by subconscious bias than they realize.

On this topic, I found an excellent PNAS paper “Persistence of false paradigms in low-power sciences” by Akerlof and Michaillat (2018). They give a detailed analysis of how, in fields where proof is relatively difficult to access, group dynamics enable false beliefs to persist. Until better data is available, false beliefs can persist indefinitely.

Discouragingly, Akerlof and Michaillat (2018) find that if a community does not cultivate a culture that values the scientific method, false beliefs can persist long after they have been disproven. They provide historical examples: medical bloodletting and radical mastectomies that were practiced into the 20th century. In both cases, the problem was that “there was no norm that a candidate’s contribution to science should be evaluated with an eye on the results of high-power scientific tests; instead, physicians’ criteria for promotion rested on a candidate’s ability to carry out existing medical practice.”


“‘[Doctors] were not prepared to accept even in principle the proposition that they should discard existing therapeutic beliefs and practices, validated by both tradition and their own experience on account of somebody else’s numbers (Warner, 1986; quoted by Akerlof and Michaillat, 2018).’”


Data-driven approaches might seem like the solution to these problems. But beware! Most data-driven approaches in subsurface engineering are observational studies, not well-designed experiments. Observational studies have their own host of problems and can themselves be the cause of persistent false beliefs (Benjamini and Hochberg, 1995; Prasad and Cifu, 2011).

With all these challenges, how can science advance at all? Because proof is possible. With a control group and a randomly selected ‘treatment’ group, it is possible to rigorously, decisively prove cause and effect (Pearl, 2009). Rigorous studies are often not practical in subsurface engineering, but we can do our best to design quality field tests and critically evaluate the results. With careful application of ‘instrumental variables,’ it is sometimes possible to use observational studies to assess causality. But these techniques are not commonly adopted across the industry, nor are they feasible for most practical applications.

Scientists have fought hard over years to build a culture based on evidence and the scientific method. It may seem trite to talk about the scientific method, but it seems that we cannot take it for granted.


  • Engineers sometimes get locked into assumptions, and struggle to drop those assumptions when they turn out to be unfounded. Overconfidence, confirmation bias, and group dynamics can cause false beliefs to persist long after they should have been discarded.
  • In the short term, the solution is to disprove false hypotheses with data. The shale industry has better subsurface data than ever before, and so we are better armed to use data and physics-based models than ever before.
  • In the long term, the solution is to cultivate a culture that values the scientific method and critical thinking. As discussed by Akerlof and Michaillat (2018), if we do not cultivate these values, then false beliefs can persist even after they have been disproven.
  • In Part 2 of this blog post, I discuss potential solutions to these challenges.


Thank you very much to colleagues who provided valuable feedback on this blog post.


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