Engineered Completion Designs Based on Mechanical Specific Energy (MSE)

What is Mechanical Specific Energy (MSE)?

MSE is a measure of drilling efficiency. It is a measure of the energy required to remove a unit volume of rock. It has long been used during the drilling phase of a well to minimize vibration (wasted energy) and maximize the Rate of Penetration (ROP). However, MSE also provides useful insight into rock properties. We use MSE completion design to optimize the design and treatment of wells during the stimulation or frac phase. This document focuses on completion design based on MSE data collected during drilling.

Calculating MSE

We calculate MSE based on routinely collected drilling log data. In other words, you need not do anything more than simply process existing data. Broadly speaking, MSE evaluates variables such as weight on bit (WOB), bit revolutions per minute (RPM), drillbit size, and ROP. The equation varies depending on whether it is in a horizontal section of the well, where bottomhole WOB is calculated, and whether it is sliding or rotating. Different companies use different equations to calculate MSE. Some also enrich their MSE data with additional data to derive proprietary insight.

Adoption of Engineered (or GeoEngineered) Completions

Horizontal wells might have a two-mile lateral with up to 100 distinct stages. In manufacturing mode, operators might drill up to 10 wells as a time on a single pad, and then zipper frac them to maximize efficiency. The size and complexity of the data involved, high-intensity drilling, and the desire to get wells on production as soon as possible make it impossible to hand-design each well using general-purpose tools like Excel. This has led to the broad adoption of geometric completion design – where design and treatment for every stage of every well is the same exact way, regardless of the unique rock properties of each stage.

MSE consultants have demonstrated, using offset well comparisons, that hand-designing wells based on MSE yield an average 19% increase in production. We believe that this is just the start. Operators must choose between speed–high-velocity geometric completion design–and production improvements–hand-designing each well at increased costs and time. Modern completion design tools now enable operators to get the best of both worlds: speed, lower costs, and reduced time-to-production. 

Drilling and completing wells is following the same evolutionary path as auto manufacturing. Initially, all automobiles were hand-built at great expense and taking a very long time. Then Ford introduced the assembly line enabling the mass manufacture of automobiles quickly and at low cost. This phase is famously captured by the phrase: “You can get it in any color, as long as it’s black.” Eventually, automotive manufacturers adopted the mass customization model, where the processes were identical and efficient, but each car had custom colors and options (interiors, engines, electronics, etc.). This is where the oil & gas industry is headed with engineered completions. Engineered completions tune stage design and treatment, based on heterogeneous rock properties along the lateral, to maximize well economics. However, mass adoption of engineered completion–for every well–requires tools that deliver all of the benefits of engineered design, at the speed and efficiency of geometric design. 

Why is MSE Valuable for Engineered Completion Designs?

Mechanical Specific Energy provides a low-cost characterization of the rock and then design your engineered completion. MSE is a class of calculations that determine rock strength, porosity, and permeability. These include confined compressive strength (CCS), unconfined compressive strength (UCS), Young’s Modulus, and Poisson’s Ratio.

MSE provides certain advantages over other methods of characterizing rock properties as described below:

  1. Low-Cost: Since we calculate MSE from drilling log information that is routinely collected, the cost is essentially zero. Other openhole logs can be quite expensive, depending on the type (equipment).
  2. Time: Unlike other forms of science, such as openhole logs, there is no time required to collect the information. Since time is money (day rates, delayed production, etc.), this is a subset of the low-cost benefit described above.
  3. Low-Risk: Any time you run anything downhole you invoke the risk of causing damage or having to retrieve the device. Since: cost = frequency of problem x (cost of intervention + cost of lost time), this too becomes a cost issue 

Because MSE provides a proxy for determining the UCS and brittleness, it’s also used as a replacement for full-waveform sonic and resistivity logs.

What Does MSE Convey About Rock Properties?

The following table describes the general value of MSE in characterizing rock

MSEHarder rockCorrelates With
HighHarder rockLower porosity
Shorter fracture length
Increased breakdown pressure
Lower ISIP
Increased brittleness index
LowSofter RockHigher porosity
Longer fracture length
Decreased breakdown pressure
Higher ISIP
Decreased brittleness index

How MSE is Used for Engineered Completions

Using MSE information in modifying stage and cluster placement (physical stage design), as well as treatment plans, while modifying treatments based on it is still an evolving art. Designing physical stage attributes based on MSE data is well-understood science.

MSE-based completion design leverages the fact that when all of the clusters in a stage are in similar rock, you increase cluster participation (more clusters producing hydrocarbons) and get better fracture propagation (increased aggregate exposure to the reservoir). This occurs because perforations in the same rock all fracture and propagate at the same treating pressure.

There are two ways to get the perforation clusters in rock with similar MSE ranges. The most obvious is to adjust cluster placement to target similar rock within each stage. The other is to adjust actual stage lengths or placement so that each stage has consistent rock. This might seem like a lot of manual labor to tune each stage and each cluster. Automating these tasks require purpose-built software for improved efficiency.

Vendors and operators have run tests comparing nearby wells completed using geometric designs against MSE-based engineered completions. While specific well results vary, MSE-based engineered completions have thus far demonstrated an average 19% increase in production versus wells with geometric completions. We believe that further improvements in design tools will enable cluster placement that is more true to MSE measurements, resulting in superior and more repeatable results.

There are additional cost advantages to MSE-based engineered completions. By placing perfs in like rock, you reduce the need for diverter to temporarily occlude the fractures that form at a lower treating pressure, so that harder rock can fracture. MSE-based completion designs eliminate diverter which requires additional material cost and pump time.

Another advantage is by reducing treating costs in poor quality or out of zone rock. Geometric design treats and designs all rock the same way. If, for example, you find that the wellbore veers out of zone for one or more stages, you could isolate those stages and use a less costly treatment. For example, if you had a single out of zone stage, you might reduce proppant by 50% and save approximately $30,000.  If you are out of zone for multiple stages, the savings could grow considerably, without negatively impacting your production. 

Practical Challenges to MSE-based Engineered Completions

While Mechanical Specific Energy is a powerful piece of information for engineered completions, you must also address practical challenges in order to maximize your results. These challenges arise where operations (what is practical) intrudes on science (theory).

Wireline Stretch

We calculate MSE based on the drillbit at depths measured by the drill string. However, perfs are shot using wireline which stretches, and the stretch increases with wireline length. The actual stretch will differ from the heel to the toe. Since MSE has one-foot precision and wireline might be off by 10 feet at the toe, this must be addressed or you might put perfs in disparate rock, resulting in subpar results, especially in the toe intervals where the stretch is most pronounced.

Shooting Perfs on the Fly

If the reservoir permits, the wireline company should avoid shooting perfs on the fly. Because this method decreases the precision of perf placement versus the stop and shoot method. However, the reservoir often dictates this.

Casing Collars

You might have a perfect MSE completion design. Then you give it to the wireline company who starts shooting perfs and runs into maybe 6 collars per stage. If the operator dictates a 4-foot buffer per collar, 2 on either side, this means you can’t perforate 24 feet. This could alter the calculation of the predominant rock type in that stage. Even worse, not considering collar locations in the design phase, the wireline operator onsite may simply shift cluster positions by up to 4 feet each. This can have a cascading impact on other perf locations, due to minimum spacing, or it may simply put those perfs in dissimilar rock types. In either case, you can quickly undermine your engineered design and deliver sub-optimal results.

Natural Fractures

Natural fractures can also undermine the goals of an engineered completion and addressing them as part of the design process.  

The good news is that addressing these challenges and DeepData’s Completion Design tool addresses them. One of my favorite quotes is quite applicable here: “The difference between theory and practice: In theory, there is no difference. In practice, there is.”

MSE: One of Many Data Sources Powering Engineered Completions

MSE completion design is a powerful and high-resolution way to characterize the geology along your completable lateral, but it is by no means the only approach. Openhole logging can provide very valuable information. You can also leverage cutting analysis (XRF, XRD, SEM, TIC, TM, etc.). Some labs will even blend rock properties with curves such as Gamma Ray, to generate pseudo-sonic curves. Cutting analysis, like MSE, is non-invasive and relatively low-cost.

The ultimate value comes from matching the actual measured results against the rock types, designs, and treatments. There are three primary mechanisms for measuring actual results. One method is via pressure graphs from treatment, this is where the reservoir speaks to you. Another approach is measuring the hydrocarbon production along the lateral. Doing this through a variety of methods, tracers (stage level insight), surface-based fluid measurement (e.g. Deep Imaging), fiber-optic (casing-attached, or in-hole), and production logs (various methods). The final, and lowest resolution approach, is to simply measure production at the wellhead. Unlike the other methods, well-level production doesn’t provide insight into which stages and clusters are contributing and to what degree that contributes.

Vendors Providing MSE Completion Design

We appreciate your insight on this list (and this page), please email new vendors to

Consulting Services

NexTier (Formed by the merger of Keane and C&J)




FractureID (based on accelerometer tool added to the BHA)

W.D. Von Gonten Labs




Software Tools for MSE-Based Completion Design



Engineered (or GeoEngineered) Completions are the future; and that future is here now. MSE is a low-cost and effective tool for characterizing rock properties. By placing the perf clusters for each stage in like rock, you get a consistent breakdown pressure across the stage, better fluid and proppant distribution, and better exposure to the reservoir. As a result, you get better production. MSE completion design is one of many data sources that will power engineered completions in the future. Continuous improvement of well economics will be ultimately be driven by machine learning that aggregates all sorts of data inputs and compares those against actual results. Starting early, building experience, and getting your data organized for machine learning will determine E&P winners and losers in the near future.