Technology Update: Drilling Data Answer to High Horizontal Logging Cost
JPT: August
Technology Update: Drilling Data Answer to High Horizontal Logging Cost (1,414 words w/byline & references)
Drilling Data Provides Answer to High Cost of Horizontal Well Logging
Joel Parshall, JPT Features Editor
The revolutionary growth of shale oil and gas development over the past 2 decades has pushed
operators increasingly toward a manufacturing approach to resource development. Thousands of
wells are drilled in the leading shale plays to offset decline rates as high as 50% in the first year
(Oil + Gas Monitor 2014). However, the advancements in drilling and completions methods that
have driven the growth of shale plays reflect two different underlying trends in regard to costs.
As an example, EOG Resources reported that its average drilling days in the Eagle Ford
Shale declined from 14.2 in 2012 to 4.3 in 2015 (EOG Resources 2015), a 70% decrease.
However, during the same span, its overall costs per well only declined from USD 7.2 million to
USD 5.7 million, a 20% decrease. So what explains this divergence? The increasing intensity
and cost of the completion program is most likely a major factor.
The methods that have driven down completion costs have focused on using less
expensive, more efficient materials and equipment. But there is also an incipient, growing
industry movement toward evaluating the potential gains by engineering the locations of stages
and perforation clusters in relation to the varying geological properties along the wellbore.
Historical evidence indicates that between 30% and 50% of perforation clusters do not
produce. With each stage costing USD 100,000 to USD 200,000, this is a substantial source of
potential capital expense savings and increased production.
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Whether the goal is to engineer completions or improve well placement, companies need
better information about the geological complexity of the reservoir to advance along the learning
curve in shale and improve results. However, the shift from traditional vertical wells to the
horizontal wells that have driven the shale boom has dramatically increased formation evaluation
costs.
High Cost of Horizontal Logging
Including rig time, the average cost to log a horizontal well with shuttle-deployed or pump-down
tools is approximately 10 times the cost of deploying wireline tools in a vertical well. Even so,
the horizontal formation evaluation logging market has been growing at a 40% annual rate,
which underscores the importance of the logging data.
However, there clearly is a problem as factory-style field development increases. An
operator possibly can afford the quarter- to half-million dollars per well to log a handful of
appraisal wells with these high-end services. But what about the hundreds or thousands of wells
that follow? There has not been an economic means of doing so at this scale with this business
model. And the recent downturn in global oil prices has heightened the problem of high logging
costs as pressure builds to reduce completion budgets.
Fortunately, the manufacturing approach to field development that accentuates the
problem of logging costs also points to a solution. With their thousands of wells being drilled,
completed, and put into production, the shale plays provide an ideal environment for collecting
data and transforming that data into insights—a practice that at least loosely reflects the larger
movement toward Big Data. Virtually every well gathers a trove of real-time drilling data, and
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when multiplied by thousands of wells, the accumulated data hold tremendous leveraging
potential to improve the drilling, completion, and production process.
Big Data to the Rescue
Witnessing the growing use of such data to increase drilling efficiency, Quantico Energy
Solutions began 2 years of research to determine whether this data could also be an information
source for formation evaluation characteristics.
As a result of that research, the company recently commercialized a logging system
called QLog, which leverages proprietary machine-learning software and a vast library of
horizontal logging and drilling data from a number of wells that exceeds those of virtually any
single oil and gas company. Developed with support from several major shale operators, the
system is economic for obtaining simulated logs on every well drilled without having to place a
tool in the well.
The system was developed by industry specialists in neural networks and openhole
logging tool design. By training neural network models with horizontal wells that possess
drilling and logging data, the QLog system is able to simulate compressional, shear, and density
logs on horizontal wells in which conventional logging tools are not run. From these three
primary logs, elastic properties such as Young’s Modulus, Poisson’s Ratio, horizontal stress, and
brittleness can be derived.
The data-driven nature of the company enables it to pursue a faster pace of innovation
than if it were to design physical logging tools. For example, the company aims to launch within
months a real-time log simulation service to assist drillers with well placement. The network
effect of various operators aggregating data through the company also leads to several
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advantages. Simulation models specific to a local field can be generated as quickly as in a few
days. In addition, operators that lack horizontal logs can leverage the existing models that have
been constructed for active plays such as the Permian, Bakken/Three Forks, and Eagle Ford
basins.
The simulated logs have been demonstrated in several blind tests conducted by operators
to show repeatable accuracy that is consistent with conventional logging tools. “The key aspect
of the system is it eliminates lost-in-hole risk and onerous costs without sacrificing measurement
quality,” said Chuck Matula, president of Quantico.
The company’s simulation models, built on drilling data, have proved significantly more
robust in the prediction of horizontal logs than the benchmark method of extrapolating gamma
ray data.
An analysis was conducted between Quantico and a major operator in the Permian Basin
comparing more than 70 log simulations that relied on gamma ray vs. the addition of drilling
data alongside the gamma ray. The QLog outperformed the gamma ray model in the vast
majority of instances (Fig. 1).
In the remaining instances, the better accuracy from using gamma ray alone proved to be
statistically insignificant. In other words, the drilling data not only increased the accuracy of the
simulated logs, but it meaningfully increased the accuracy compared with a traditional approach
of relying on gamma ray data alone.
Case Study: West Texas
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An operator needed an economic means of generating geomechanical logging measurements on
wells in the Delaware Basin of west Texas. Historically, the operator had run conventional
openhole logs on its wells in the basin. However, with the steep drop in oil prices, the cost of
conventionally logging a hole deviation of 90° and a lateral section of more than 5,800 ft had
become uneconomic. The operator needed to reduce expenses substantially.
Using the QLog system, with local-area data contributed by the operator supplementing
Quantico’s library of well data, log simulation models were produced within a week without
running a tool into the hole. The models’ inputs were soon customized to leverage specific
measurements collected by the operator during drilling.
The model was used to produce simulated compression, shear, and density logs.
Subsequently, Quantico used its proprietary QFrac software, which uses the simulated logs to
automatically generate elastic properties and recommend engineered stage locations, to provide
the operator with Young’s Modulus, Poisson’s Ratio, horizontal stress, and brittleness data (Fig.
2). The operator was thereby able to examine optimal stage length and perforation cluster
positioning.
By the use of the simulated logs, the operator was able to obtain critical formation data of
a quality consistent with conventional logs and reduce logging-related costs by 90%.
Looking Ahead
“We believe that we are only in the ‘first innings’ of identifying data-mining methods that solve
salient problems for oil and gas companies,” Matula said. With such methods, similar solutions
may be leveraged for areas such as drilling optimization, geophysics, and production logging.
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“The success or failure of future data-mining solutions lies largely in identifying where
data exists but may be underutilized—rather than requiring new measurements to be taken—and
then providing industry professionals with data they can use in existing workflows and software
platforms,” Matula said. “If so, then the rewards from even marginal capex savings or higher
production on each well can be replicated on thousands of wells.”
Such innovative methods and relentless attention to driving efficiencies, he said, are
critical to the economic future of shale resource development, especially in a low-price
environment.
References
EOG Resources 2015. http://www.eogresources.com/investors/slides/InvPres_0315.pdf [Report not
found when link was entered online.]
Oil + Gas Monitor 2014. Oil Demand and Well Decline Rates Ensure Strong Outlook for Oil
Industry. Oil + Gas Monitor, 5 November, http://www.oilgasmonitor.com/oil-demand-andwell-decline-rates-ensure-strong-outlook-for-oil-industry/8075/ (accessed 10 June 2015).
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Fig. 1—Most log simulations showed improved accuracy, when drilling data were added to
gamma ray data (blue). Simulations with only gamma ray data (red) exhibited marginally better
accuracy in a few cases. Image courtesy of Quantico Energy Solutions.
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Fig. 2—Tracks 2-4 display comparison of simulated (red) and conventional logs (blue) where
elastic properties including Young’s Modulus, Poisson’s Ratio, horizontal stress, and brittleness
data were automatically generated. Image courtesy of Quantico Energy Solutions.