American Oil and Gas Reporter - November 2019 - 43

SpecialReport: Cybersteering
· Building a graph;
· Calculating edge costs (weights);
· Finding the shortest path between
the start and end vertices; and
· Visualizing the results.
Graph construction is partitioned across
Spark nodes and scaled linearly with
cluster size. This enables fast graph creation
for very large graphs. A 10,000-foot lateral
with hundreds of thousands of vertices
and hundreds of millions of edges can be
generated in only a few minutes.
Edge cost evaluation also is partitioned,
but individual edges require iteration
across measured depths within a block.
This results in linear time execution for
each partition and requires optimized
partition sizing to find solutions in a reasonable time.
The most technically complex part of
the solution occurs when traversing the
graph to find the shortest path. Shortestpath algorithms proceed sequentially by
nature and depend on previous path information to solve. This is difficult to accomplish in a distributed environment
when a graph is partitioned. To overcome
this limitation, cybersteering uses a unique
distributed shortest-path algorithm.
Promising Results
The initial results of the cybersteering
approach are very promising. Devon Energy performed a study in which 60 wells
across multiple formations were steered
with the new approach. Previously, these
wells had been manually geosteered and

the results were compared. In regions
with well defined, clean gamma traces,
cybersteering performed extremely well,
producing steers very similar to its human
counterparts (Figure 3). Both steers leave
the target formation and re-enter at similar
locations after landing the curve. Both
steers also tend to follow the formation
slightly up dip along the lower quarter of
the target zone.
Data quality and parameter choices
have an impact on cybersteering (of
course, human geosteerers also can be
sensitive to data quality). In areas with
muddy or erratic gamma traces, it is important to more aggressively smooth incoming gamma traces. Normalization
should be as accurate as possible, especially in areas where the type log shows
little to no variation in gamma along the
vertical depth, making it difficult to confidently place wellbore position. Figure
4 shows the potential impact of poorquality inputs.
While Cybersteering doesn't come without faults, the areas in which it falls short
seem to have clear paths for improvement.
Overall trends could be easier to detect
with more aggressive smoothing or other
trending methods. It also is currently unable
to deal with faults, but vertical edges with
an associated cost could be constructed
within the graph database. These ideas,
along with incremental improvements in
other areas, could guide cybersteering to
usability in an even wider variety of formations and drilling operations.

FIGURE 4
Example of Impact of Poor Data Quality on Cybersteering
6,460
6,400

6,470
6,420
6,480
6,440
6,490

6,460
6,500

6,510

6,520

Top Bed
Bottom Bed
Well Trajectory

6,480

6,000

8,000

10,000

12,000

14,000

16,000

18,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

1.2
1.1
1.0
0.9
0.8
0.7

Wellbore Gamma
Type Log Gamma

Cybersteering comes with numerous
advantages. The geosteering process as
the industry knows it today is widely
variable. The same person geosteering
the same well can produce different results
of varying quality based on a range of
factors. Cybersteering can provide accurate, repeatable results. As with any automated process, building trust in the automated system takes time, so gradual
implementation and adoption is recommended. Of equal importance is end-user
understanding of how the system works.
Understanding why cybersteering makes
the decisions that it does along with its
potential flaws are incredibly important
to interpret results.
Cybersteering has major potential in its
current iteration. Further improvements to
the technology only will expand its usability
as a primary geosteering methodology in a
wider variety of geologies. Using a graph
database to represent spatial relationships
in drilling operations also has proven to be
a valuable tool and has potential application
outside of geosteering.
❒
TRISTAN ARBUS is a data scientist
at Devon Energy Corp. He designs
and builds production-ready machine
learning and statistical models for
drilling, supply chain, human resources,
communications and other applications.
Before joining Devon in 2014 as a
drilling and project engineer, Arbus
served as a drilling engineer at Consol
Energy and as a mechanical engineer
at BUG-O Systems International. He
holds B.S. degrees in physics and in
mechanical engineering from Johns
Hopkins University.
STEVEN WILSON is an advanced
analytics and data sciences engineer
at Devon Energy. He designed and
developed the cybersteering automated
geosteering software. Before joining
Devon in 2015, Wilson served as information technology manager at
Chesapeake Energy and as a software
developer and air quality and water
treatment engineer at OGE Energy
Corp. He holds a B.S. in chemistry
from the University of Oklahoma and
an M.S. in chemical engineering from
Oklahoma State University.
NOVEMBER 2019 43

American Oil and Gas Reporter - November 2019

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