Lost amid the incessant rhetoric over the energy transition, operators continue to wrangle tricky wells and exploit the latest in digital wizardry to beef up drilling performance in ostensibly straightforward wellbores.
Indeed, whether it's effectively drilling extended-reach wells with all the inherent instability and lost circulation issues or applying digital technologies to deliver consistency across all rigs within an operator's asset, the outside noise takes a back seat to maximizing the value of wells throughout their productive life. At least, that was the message delivered last November at IADC's Drilling Engineering Committee (DEC) technology forum, which pointed out how "engineers are increasingly focusing on designing and delivering wells to maximize the return on investment, while dealing with a more stringent regulatory environment."
A comparison of two widely diverse drilling projects highlighted that focus.
“Walking a fine line.” The successful drilling of a shallow, extended-reach gas well offshore Malaysia exemplified the balancing act that operators must often accept when trying to optimize drilling parameters, while corralling the cornucopia of high-angle ERD-related hazards.
"These wells are notoriously challenging, requiring ECD management, hole cleaning and the prevention and management of lost circulation. We had to walk a fine line on balancing the risks between wellbore instability and lost circulation," Michael Yao, a rock mechanics advisor for operator Hess, told the hybrid forum. "All the challenges were lumped together, particularly the pore pressure versus the collapse pressure. Highly deviated horizontal wells also tend to have more breakouts and require higher mud weights."
Pre-drilling planning began with an evaluation of two offset wells with similar trajectories, both drilled with an 11.4 lb/gal mud weight. The first well had been drilled trouble-free at an average ROP of 100 ft/hr, while the second was drilled at 150 ft/hr, resulting in lost circulation, with a 13.7-to-13.8 lb/gal ECD overwhelming the equivalent static density (ESD) of 11.8 lb/gal.
The targeted well was programmed for an 8½-in. hole, with TD at 12,642 ft, MD (3,925 ft, TVD), and 3,300 ft of open hole. The 9⅝-in. casing was to be landed at 9,282 ft, with a 78o inclination. Before reaching reservoir sand in the lateral, the drilling team would have to maintain the stability of roughly 1,000 ft of pressured and heated shale that would be penetrated at high angle.
Priority was placed on optimizing both mud weight and the drilling parameters, to manage ECD and hole cleaning. To prevent losses in the weaker zone, well designers programmed Hess's own formulation of the widely used stress cage wellbore-strengthening methodology that provides pressure containment by treating fragile areas with drilling fluids with engineered, particulate lost circulation materials (LCM). It became clear that to successfully land the well as programmed, however, some trade-offs would have to be considered.
"How much breakout can be tolerated, and how can we minimize ECD with optimal ROP and flowrate, without compromising hole cleaning? How much wellbore strengthening can we achieve to push the fracture gradient higher, and which side of the risk do we want to take? If you don't have a choice, you have to decide if you'd rather deal with wellbore collapse or lost circulation," Yao said.
To reconcile the ECD requirement on lost circulation, Hess selected a 10.8 lb/gal surface mud weight for the 8½-in. hole. The final drilling parameters called for the well to be drilled at 100 ft/hr, but this would drop to 50 ft/hr upon implementing the stress cage.
"We would use Hess's stress cage formulation to prevent losses, in case the low-side pore pressure fracture gradient is encountered. Whether to strip out the stress cage would be determined, based on results from real-time pressure measurements, once the competent sand is exposed," he said.
The well was drilled to TD uneventfully, under controlled drilling parameters, with no losses or wellbore stability issues. While the stress cage was implemented as planned, lower-than-expected ECD values later rendered it unnecessary.
Developing fleet uniformity. In a far less extreme drilling environment, ConocoPhillips looked for a way to develop uniform rig performance in the vertical sections within its South Texas Eagle Ford asset, which takes in the fragile Wilcox sands. "The vertical section, historically, is where we've had some really high-performers, but we just didn't have uniformity in ROP across all our rigs," said Eric Muller, drilling engineer turned data analyst. "We wanted to push the threshold as far as we can, without breaking down the Wilcox."
To that end, the company turned to machine learning and advanced modeling techniques, with the aim of optimizing ROP without incurring BHA damage. "We wanted our data scientists to look at all our ERD and formation data to see if there was something our high- performing rigs were doing that could be used throughout," he said.
Using big data to first look at historical drilling performance, the effort began with a dataset incorporating 260 wells with more than 8.7 million data points, later filtered down to 116 wells and 138,000 data points. The process evolved into identifying the factors that could be controlled—primarily differential pressure—followed by flowrate and weight on bit (WOB).
The eventual model concluded that maintaining a minimum differential pressure of 850 psi would yield an incremental 31 ft/hr, which would require the motors and BHA to be modified accordingly. A minimal flowrate of 600 gpm and a minimal WOB of 25,000 lb (30,000 lb maximum) were also predicted to increase ROP by 10 ft/hr and 4 ft/hr, respectively.
The guidance developed through the data-driven machine learning model accurately predicted ROP in vertical sections, with a mean absolute percentage error of around 12.5%. After rollout across the fleet, the operator has seen a consistent 26% increase in ROP in the vertical sections, without incurring any BHA damage. "Almost overnight, we saw the performance of our rigs skyrocket. We set like nine vertical records in a row in the first three weeks," he said. "It was more of a cultural shift and (created) competition between crews."
ConcoPhillips is now developing similar models for the curve and lateral sections, with a vision to use artificial intelligence (AI) and machine learning to eventually create closed loop drilling and completions.
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