De-Risking Horizontal Drilling with Machine Learning in Unconventional Plays

Horizontal drilling in unconventional reservoirs comes with significant risks and uncertainties. Yet, it also generates massive amounts of data that hold the keys to smarter decision-making. In this article, we explore how machine learning (ML) can help de-risk horizontal drilling by addressing two major risk categories: (1) well placement (where and how wells are drilled) and (2) drilling & completion design (how wells are drilled and fracked). We’ll discuss why traditional deterministic models often fall short in complex shale plays, and how data-driven ML approaches can learn from thousands of past wells to guide optimal well placement, spacing, and completion strategies. Finally, we’ll highlight how SubsurfaceAI’s solutions leverage well, seismic, and production data to build predictive models that turn subsurface uncertainty into actionable insight.

Understanding the Risks in Horizontal Drilling

Well Placement Risks: Deciding where to land horizontal wells – including their location on the pad, lateral orientation, and spacing from other wells – is critical. If a well is placed in a poor-quality zone or too close to another well, it may underperform or interfere with neighboring wells. Optimal well spacing should balance drainage and interference: wells too tightly spaced can suffer “frac hits” and pressure communication, while wells too far apart leave valuable hydrocarbons unproduced. Likewise, well orientation matters – drilling the lateral in the optimal direction (often aligned with maximum horizontal stress) promotes more effective fracturing and reservoir contact. In short, the placement of horizontals is a high-stakes puzzle: getting the spacing, azimuth, and landing zone right can dramatically improve a project’s success, whereas suboptimal placement can lead to lost recovery or costly issues.

Drilling & Completion Design Risks: Even with a well in the right spot, how you drill and complete it poses another set of risks. Decisions about drill plans and frac designs – such as the drilling mud type, bit/BHA choices, or the hydraulic fracturing method, fluid, proppant, stage spacing, cluster density, pump schedule, etc. – have huge impacts on well performance. In unconventional plays, operators traditionally relied on engineering intuition and deterministic models to design fracs and wells. However, these methods often simplify the geology and can fail to capture how complex rock properties influence fracking outcomes. The result is that two wells with the same design may perform very differently if their rocks differ. Getting the drilling and completion design wrong can mean suboptimal stimulation, rapid decline, or even well failures. Thus, there’s a pressing need to “de-risk” these decisions by better predicting which designs will work best for a given geological setting.

Why Traditional Models Fall Short in Shales

Historically, subsurface teams have used deterministic physical models (e.g. basic reservoir simulators or empirical formulas) to plan horizontal wells and fracs. While grounded in physics, these models struggle with the high complexity and uncertainty of shale reservoirs. Unconventional geology is notoriously heterogeneous – with nanopore networks, natural fractures, varying stress regimes – leading to nonlinear, high-dimensional, and uncertain data that traditional methods have trouble handling. Deterministic models often require simplifying assumptions that gloss over geological complexities and the coupled factors affecting production. In practice, this means engineers might miss critical interactions or be overly confident in models that don’t fully reflect reality.

Machine learning offers a way to embrace this complexity rather than simplify it. Instead of prescribing a rigid physics-based model, ML algorithms can learn patterns directly from data. Shale plays produce an abundance of data (drilling logs, core, seismic, production history) that encodes the subtle geological interactions. Deterministic models may not capture those nuances, but ML can discover them by training on real outcomes. In other industries, predictive analytics have excelled at recognizing complex patterns and making reliable forecasts – and the same is proving true for oil & gas. In short, when traditional models hit their limits, data-driven ML models step in to extract insights from the messy reality that is the subsurface.

A Data Goldmine: Thousands of Wells as Learning Examples

One advantage in today’s unconventional plays is sheer scale. Major shale basins have seen thousands of horizontal wells drilled, each effectively a tiny experiment generating valuable data. For example, the Eagle Ford Shale alone has data available from over 4,000 wells in public datasets used for machine learning studies, and other plays like the Permian have even more. This wealth of information – well logs, microseismic, production profiles, completion reports – is a goldmine for ML. The more data, the better ML can learn the subtle cause-and-effect relationships that humans might overlook.

Crucially, ML algorithms thrive on “big data” to capture patterns that deterministic models miss. They can consider dozens of variables simultaneously (rock properties, drilling parameters, completion metrics, etc.) and find non-obvious correlations. For instance, an ML model might learn that a certain range of brittleness index combined with a specific frac fluid and cluster spacing yields above-average recovery – insights that would be hard to derive from theory alone. In essence, the unconventional arena’s high well counts and data volumes make it ripe for machine learning, turning the complexity and scale into an advantage. Instead of being overwhelmed by data, companies can let ML algorithms comb through it to find the signal in the noise.

ML to the Rescue: Reducing Risk through Data-Driven Insights

Machine learning can assist subsurface teams at each stage of well planning and execution. By learning from past successes and failures, ML models help identify what “good” looks like under various conditions – thereby reducing the guesswork (and risk) in future decisions. Let’s look at how ML specifically addresses our two risk categories:

1. Finding Sweet Spots & Optimizing Well Placement

One of the most powerful applications of ML in unconventionals is sweet spot identification. Sweet spots are those areas or zones in the reservoir with the best chance of high productivity (due to favorable thickness, porosity, pressure, brittleness, etc.). Identifying these manually is challenging, given the interplay of many geological factors. ML shines by analyzing vast datasets of well outcomes and geology to pinpoint the patterns associated with the most productive wells. In fact, studies have shown that ML algorithms significantly improve workflows for evaluating sweet spots in complex reservoirs. By linking well production data with geology and seismic attributes, an ML model can learn which combinations of features lead to high performance.

How does this reduce risk? First, it helps high-grade acreage – i.e. focus development on the best zones and avoid areas likely to underperform. This directly cuts the risk of drilling unproductive wells. Second, even on a given pad, ML can guide where to land each lateral and how to steer it. For example, an ML analysis might reveal that wells targeting a certain stratigraphic layer or structural position consistently yield better EURs. Armed with that knowledge, geologists and drilling engineers can plan trajectories that maximize time in those sweet zones.

Furthermore, ML can assist in well spacing and orientation optimization. Rather than rely only on simulator forecasts, operators can train ML models on production outcomes from various spacing pilots. The model learns the spacing at which wells start strongly interfering with each other versus the spacing that leaves significant undeveloped reserves. The optimal is often somewhere in between, and ML can delineate that sweet spot. Similarly, an ML model might analyze directional data and find that wells oriented within, say, 10° of the maximum stress direction perform best (thanks to enhanced fracture complexity), whereas wells drilled at large angles to the stress underperform. In practice, engineers at the decision table can use these data-driven insights to choose well spacing and azimuth that balance the risks – avoiding both the productivity loss from overly conservative spacing and the interference issues from overcrowding.

Notably, machine learning doesn’t operate in a vacuum; it complements domain knowledge. A seasoned geologist might suspect where good rock is, and an engineer might have an intuition on spacing – the ML model provides additional evidence by crunching the numbers across many wells. The result is a more informed well placement strategy, where every horizontal well is positioned and spaced with the benefit of lessons learned from thousands before it. This data-driven approach directly tackles risk #1: it increases the probability that each new well hits its mark (literally and figuratively) by being in the right place, pointing the right way, and spaced properly relative to its neighbors.

2. Improving Drilling and Completion Designs with ML

The second category – drilling and completion scheme – is equally ripe for machine learning innovation. Modern unconventionals involve complex, multi-stage frac jobs and tailored drilling approaches. Over the years, operators have tried myriad combinations of fluids, proppants, stage lengths, cluster counts, pump rates, etc., generating a trove of data about what works and what doesn’t. ML can digest these varied datasets to find patterns that lead to better well performance, thus guiding future design decisions.

One way ML reduces risk here is through production forecasting and design optimization. For example, a team might use historical well data to train an ML model that predicts a well’s 12-month production given its completion parameters and geologic inputs. If the model is accurate, engineers can then virtually test different completion designs before executing them in the field, seeing which recipe is predicted to yield the best outcome. This is essentially a what-if tool: What if we increase fluid volume? What if we tighten cluster spacing? – the ML model provides an answer without the cost of real experimentation. Researchers have even combined ML with optimization algorithms (like particle swarm optimization) to automatically search for the best combination of frac parameters that maximize net present value. Such workflows can rapidly hone in on an optimal design that might take years of trial-and-error to discover in the field.

Another benefit is real-time risk monitoring. As drilling and frac operations proceed, ML models can analyze sensor data and drilling parameters to flag anomalies or predict problems (like stuck pipe, kicks, or frac hits) before they happen. For instance, by training on historical instances of drilling issues, an ML system can learn the warning signs (subtle changes in torque, pump pressure, etc.) and alert engineers to take corrective action early. This proactive approach reduces the risk of costly non-productive time or well damage during the operation.

At the completion stage, ML has been used to predict fracture growth and reservoir response. By integrating data like treatment pressure, microseismic events, and well logs, ML models can infer the created fracture network’s effectiveness and suggest adjustments. In one field study, an ANN (artificial neural network) was used to anticipate the production outcomes of different fracturing strategies in shale, finding strong correlations between certain treatment parameters and total production. These insights help completion engineers design fracs that are tailored to the reservoir’s characteristics, rather than relying purely on generic templates.

In summary, ML helps demystify the “black box” of shale completions by leveraging what’s worked (or not) across many wells. It reduces risk by providing evidence-based guidance for drilling and frac design: identifying which parameters most influence success, predicting the results of design tweaks, and even optimizing the design for a given objective (e.g. maximize EUR or minimize cost). The end result is a higher likelihood that each well is drilled efficiently and completed right the first time, hitting top-tier performance instead of falling short due to suboptimal design choices.

SubsurfaceAI’s Approach: Data-Driven Decisions for Every Well

At SubsurfaceAI Inc., we have embraced these machine learning techniques to help oil and gas companies de-risk their drilling programs. Our solutions are built to ingest and integrate diverse subsurface datasets – geological, geophysical, and engineering – and turn them into predictive models for optimal well planning. For example, our platform can combine well data (logs, core, past production), seismic attributes, and completion data to map out key reservoir properties and sweet spots. By integrating seismic inversion results with well logs, we generate high-resolution reservoir property grids and maps. These ML-derived maps highlight where the rock is most promising (e.g. high brittleness, good saturation, etc.), guiding geoscientists on where horizontals should be landed.

Beyond static maps, SubsurfaceAI’s workflows leverage machine learning to forecast well performance and recommend drilling/completion parameters. We use fit-for-purpose ML models that take into account near-wellbore seismic attributes, adjacent well production, and completion designs to predict the productivity of new well locations. In practical terms, this means we can input the coordinates of a proposed well (along with planned orientation and design), and our model will estimate expected production based on analogs and patterns learned from the data. If the prediction is sub-par, our system can suggest adjustments – perhaps moving the well a bit, or altering the completion plan – to target a better outcome.

Importantly, our approach is not a one-size-fits-all black box. We recognize that each play and each operator have unique nuances. That’s why our platform is modular, allowing customization and incorporation of your proprietary knowledge. Whether it’s a particular seismic attribute you trust or a specific operational constraint, SubsurfaceAI can incorporate those into the machine learning models. The goal is to create a holistic, data-driven decision support system that becomes a natural extension of your team’s workflow. Instead of pouring over disparate spreadsheets and maps, your team gets an integrated dashboard of ML insights – sweet spot maps, optimal well orientations, predicted EUR heatmaps across your acreage, and more – all grounded in real data.

By using SubsurfaceAI’s predictive analytics, subsurface managers and engineers can make decisions with greater confidence. You’re no longer betting on a gut feeling or a simplified model; you have a wealth of data-driven evidence at your fingertips. This dramatically reduces the risk of unforeseen surprises. In short, we aim to take the luck out of the equation and replace it with knowledge.

Heat map of predicted productivity across a lease area. Brighter colors indicate higher predicted well performance (“sweet spots”) based on an ML model, whereas darker areas are lower potential. Visual tools like this help teams quickly identify where new horizontal wells will likely be most productive, enabling smarter well placement decisions.

Real-World Impact and Embracing ML in Decision-Making

Machine learning for horizontal drilling is not just a theoretical exercise – it’s delivering tangible results in the field. Companies that have integrated ML into their drilling programs report better forecasting accuracy, improved well performance consistency, and more efficient operations. By learning from the collective experience of many wells, ML models can often predict outcomes more reliably than traditional models. In one Eagle Ford study, a random forest ML model provided more reliable EUR predictions than a conventional regression approach, highlighting how data-driven models can capture reality better and reduce uncertainty in forecasts.

The value of ML is especially clear when making big development decisions. For instance, selecting the next drilling locations in a large asset can be a multi-million-dollar decision. ML-derived sweet spot maps and productivity heatmaps give management a high-level view of where the best returns are likely to lie. This turns what used to be an experience-based guess into a more quantitative, evidence-backed selection process. As noted by Emerson in a 2015 case study, sweet spot recognition is essential to reducing uncertainty and improving field economics, and predictive analytics can integrate geology, geophysics, and engineering data to make this process far more effective. In practical terms, that means fewer missed opportunities and fewer costly missteps.

Integrating ML into the decision loop also fosters a culture of continuous learning. Each new well’s data is fed back to update models, so predictions get better over time. Teams begin to trust the models as they see successful recommendations (e.g., a suggested spacing scheme that resulted in strong wells). Over time, this creates a virtuous cycle: more data → better models → better decisions → even more good data. Companies that leverage this cycle can gain a competitive edge, extracting more value from their acreage with less trial-and-error.

Perhaps most importantly, ML brings a probabilistic mindset to what has traditionally been a deterministic field. Instead of planning one development scenario and crossing fingers, ML allows you to evaluate many scenarios and understand their probabilities of success. Risk becomes something you can quantify and visualize (through probability maps, for example), which is incredibly powerful for strategic planning. As one recent analysis put it, by generating data-driven sweet spot likelihood maps and predictive models, geoscientists and engineers can not only plan optimal wells but also assess the confidence in those predictions for risk assessment and scenario ranking. This means decision-makers can see not just a recommendation, but also the level of uncertainty associated with it – enabling informed risk-taking where appropriate or more cautious strategies when uncertainty is high.

In summary, the real-world impact of machine learning in horizontal drilling is a more rigorous, data-informed approach at every step. From leasing and planning to drilling and completing wells, ML helps ensure that choices are grounded in knowledge gained from the collective industry experience. It’s about working smarter – using computing power and algorithms to do the heavy lifting of pattern recognition – so that human experts can focus on creativity and high-level strategy. Those who adopt these tools are finding that the subsurface’s former unpredictability is now much more manageable, with the odds of success improving in their favor.

Conclusion: A New Era of Data-Driven Drilling – Get Involved

The unconventional revolution has always been about pushing boundaries – in technology, in scale, and now in intelligence. Machine learning is ushering in a new era where we can de-risk horizontal drilling not by avoiding uncertainty, but by harnessing it. By learning from thousands of wells and countless data points, ML gives us clarity on optimal well placement and completion design in ways that previous generations of tools simply could not. It transforms the subsurface from a question mark into a map of probabilities and predictions, guiding us to the sweetest spots and the best methods to exploit them.

For geologists, geophysicists, engineers, and asset managers, this means your expertise is amplified. You can test hypotheses in silico, trust that your next well location is backed by robust pattern recognition, and design fracs with confidence that they’re tuned to your rock’s unique signature. The result? Better wells, lower risk, and higher returns – the trifecta every operator is chasing.

At SubsurfaceAI, we are proud to be at the forefront of this data-driven transformation. Our mission is to empower subsurface teams with AI/ML tools that blend seamlessly with their workflow and dramatically improve decision quality. We’ve seen first-hand how our predictive models and sweet spot maps have helped clients plan successful campaigns, saving millions in avoided missteps and boosting production outcomes.

Call to action: If you’re ready to leverage the power of machine learning to de-risk and optimize your horizontal drilling projects, we invite you to reach out to SubsurfaceAI. Let’s collaborate on a pilot, dive into your data, and uncover actionable insights for your fields. Whether you want a demonstration of our platform’s capabilities or a consultation on a specific challenge, our experts are here to help. Don’t let uncertainty hold your development back – embrace the data, harness ML, and drill with confidence. Contact SubsurfaceAI today to start the journey towards smarter, more profitable unconventional development. Your next breakthrough “gusher” might just be a model prediction away.