Operators in North American shale basins have an opportunity to take advantage of the vast amount of data being collected from operations to improve margins during this period of falling oil prices. Thousands of sensors used in the drilling and completions phase, at the wellhead, and along the supply chain create a data-rich environment. The challenge in creating value from these data stems from creating, managing, linking and automating changes in these core data environments so they can be repurposed to support strategic initiatives as well as daily operations.

With conventional resources, geoscientists evaluate plays to find areas with a hydrocarbon charge, a reservoir and a trapping mechanism. The desired acreage is pursued, prospects are high-graded and the best are drilled. Each function does its job and passes the results to the next function in the value chain. The departmentalized nature of the work, the measured pace at which prospects are evaluated and the rate at which wells are drilled leave time to manage the data as a prospect works its way through the life cycle.

In the unconventional world, all three factors (charge, reservoir and seal) are combined in the one shale formation. Hydrocarbons are generated and retained within the shale, so the prospectors do not have to worry about the migration and separate trapping of the conventional reservoirs. Finding the hydrocarbons in a shale play is not the difficult part—the challenge is finding the most productive sweet spots within the areal extent of the shale basin.

Data analytics

The goal of shale operators is to accelerate the pace of lease acquisition to drilling, completions and production. In many ways, the shale play is a “data play” as well as an application of manufacturing mindset to E&P processes. Given the fast fall-off of production from these wells, there must be a standard, repeatable process of production to construct wells fast enough to make shale plays economical. Consequently, the “factory” or “assembly line” analogies are relevant.

After data integration, the business can take operational efficiency to the next level through analytics. Analyzing drillbit performance, motor reliability and mud composition can reduce trips and thereby reduce costs and the time to drill a well. Analyzing multistage frack jobs can determine what approach and designs work best for a particular area.

Modeling only goes so far as this is an area of almost constant experimentation. Advanced analytics that include financial data on costs and revenue can optimize the entire life cycle.

The factory mindset does not stop just with the drilling operation activities conducted by the operator. This factory relies on an agile supply chain (to build the pad and drill the wells) and a business value chain to take the product to market. To become a market leader requires not only cross-disciplinary cooperation inside the operating company but collaboration with vendors providing products and services along the way.

Data management

Visiting the drilling operations brings the physical factory to life. Equipment is packed on the drill pad so tightly that it can be difficult to move around. Pumping units, sand and chemical storage units, the drilling and completions rigs, the trailers for the engineers and support staff, and the water pit all dominate the visual scene.

The digital “mirror image” of this operation may be harder to see, but it is just as important. Sensors and process control units link the various pieces of equipment on site, while communication services link in experts back at the central office. The data environment does not just hold the current state of operations; historical data from similar wells are used to build model-based simulations to help guide the current activity.

This digital environment connects not only operator personnel; it may include experts from service companies, partners and even regulators. A robust data foundation and an integration framework provide the underpinnings to make all actions run smoothly just as much as the control trailer does for the physical environment.

Effective data management success in the unconventional world hinges on four components: governance, integration, business process management and analytics. “Governance” is a term that borders on overuse; however, data governance remains lacking in many companies. Implementing data governance does not mean creating a data-focused bureaucracy. It means translating business needs into adhered-to business and data management processes. It means defining roles and responsibilities for collecting and managing data, from structured to documents to the transaction to field measurements. It means applying data standards transcending individual disciplines.

In most companies data integration means merging data from the production and accounting worlds. For the unconventional world this level of integration falls woefully short. Because of the model-based nature of successful unconventional developments, the integration of data must span the entire value chain.

Linking the many data repositories associated with unconventional wells depends on master data management. Well data needs to be tracked consistently throughout systems from prospect to divestiture. Linking a traditional well identifies things such as drilling spacing unit, well name, unique well identifier, regulatory permit identification and well name used in financial enterprise resource planning or land/lease databases. The unconventional play is more than the sum of its parts—it is a way of doing business that is fully integrated with the ability to correlate data across the enterprise.

Model-based approach

The race to acquire prospective acreage makes an early entry into unconventional plays and locating the sweet spots critical. Prospective shales tend to cover large areas with drilling programs measured in the hundreds of wells per year, dozens of rigs operating at the same time and thousands of wells required to develop the shale reservoir fully. Success depends on a model-based approach that holistically considers fractures, well designs, mineralogy, completion designs, production data and reservoir patterns. With the business operating at such a scale and pace, sustainable success requires the implementation of well-defined data management capabilities.

Accurately collecting information and making it available for analytical use becomes the basis for optimization scenarios, even simulations. Much of the data collected from real-time applications during the drilling process itself will become even more useful with the advent of big data applications for the oil and gas industry.

Data will allow companies to analyze just-in-time options, better control drilling programs and rig schedules and have much better insight into supplier contracting possibilities. However, without first putting the foundation in place to collect all of the relevant data, none of these new possibilities can become reality.

These analyses must not be limited to traditional reporting since existing systems do not scale easily. The sheer volume of data requires systems capable of performing multivariate analysis in a timely manner (in days, not months).

New technologies need to be tested and used during a drilling program instead of waiting until next year’s program to make changes that will bring significant benefit. Some companies now drill more than 1,000 wells a year in shale plays, completing more than two wells per day. The difference between $10.5 million per well and $11 million per well over an entire year’s operations can be $500 million.

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