Access to real-time downhole data has enabled well teams to drill increasingly complex well profiles in extremely challenging environments while improving efficiencies and economics. Supporting operations such as wellbore intervention must keep pace helping to maximize the efficiency, cost effectiveness and resulting productivity of overall well construction operations. A recent project in the deepwater Gulf of Mexico (GoM) illustrates the benefits that can accrue when real-time downhole data are gathered, analyzed and applied to the decision-making process in a wellbore intervention.

Removing uncertainty from wellbore interventions

Operators typically guide intervention operations with analog surface gauges. While these gauges are calibrated in thousands of pounds, it’s not unusual to have to retrieve objects from the well weighing only a few hundred pounds. Even under the best conditions this weight barely registers on standard instrumentation. Additional error factors are often caused by frictional losses and pipe stretch. The resulting uncertainty increases the difficulty of intervening effectively in extended-reach wells.
Doglegs, horizontal sections and depth obscure the activity at the end of the tool string. Modeling can help, but models are only as good as the assumptions that go into them. If the model has never been validated, it can contribute to poor decisions.

Baker Hughes developed the Sentio smart intervention service to mitigate risks and save nonproductive time (NPT) by capturing downhole data in real time and communicating them to the surface for diagnosis and interpretation by trained personnel. The development of this service was a step-change toward a future where problems can be identified and analyzed and best solutions determined, applied and verified, all in a single intervention run. The smart intervention service enables access to the same type of real-time downhole data that have been available for decades in drilling systems. Coupling the data with the engineering expertise of the company’s wellbore intervention team helps to improve the efficiencies, safety and economics of offshore intervention operations.

A job incorporating smart intervention begins with the operator setting the initial surface parameters for the job. Parameters such as weight, torque and RPM are monitored not only by the surface gauges but also by downhole sensors embedded in the tool string. These sensors take measurements at the end of the string, which is the real point of interest. Those measurements are sent back to the surface in real time via high-performance mud pulse telemetry systems and are displayed to the operator in easy-to-read formats. Based on the data and the critical parameters for that application, the operator can adjust the surface parameters to achieve the desired output downhole, thereby achieving optimal job performance.

The availability of real-time measurements allows the operator to detect problems early and correct them before they develop into something more serious and to make real-time data-based decisions throughout the job. The smart intervention system measures parameters such as:

  • Tension, compression and torque, which are critical for milling operations;
  • Bending moments, which are useful for estimating dogleg severity in a well;
  • Pressure in both the annulus and the bore to help calculate equivalent circulating density, which helps avoid fracturing the formation and the resulting fluid and time losses;
  • Vibration, which is a useful monitor for milling dysfunction;
  • Orientation, which is helpful for whipstock placement and fishing operations; and
  • Gamma waves, which identify landmarks such radioactive pip tags in casing collars and enable correlation to open-hole gamma logs for depth verification.

The concept of technology and knowledge integration extends beyond the rig and downhole tools. With modern satellite communications, the data feed can be securely transmitted worldwide to the company’s remote operation centers and customer locations, which allows for global expertise to be applied to data interpretation, modeling and application engineering, regardless of the job location.

Intervention challenge in the GoM

The Sentio smart intervention service was recently applied in a deepwater operation in the GoM, leading to a significant reduction of NPT and faster operations.
The operator was experiencing difficulties in the final completion of a well and opted to bypass the troublesome section by performing a casing exit with a window depth of 7,865 m (25,803 ft) at a 45-degree inclination. A whipstock had been left in the hole from a previous failed attempt at approximately 396 m (1,300 ft) above the target depth. Given the depth and well angle involved, the operator elected to use the smart intervention service to reduce uncertainty during the whipstock fishing operation and subsequent whipstock casing exit.

A fixed-lug retrieval tool, downhole sensor sub and telemetry system were quickly deployed to fish the whipstock. The retrieval tool fits into a dovetail slot on the whipstock, which is 2 ½ in. wide and has approximately 1/16-in. clearance.

Fishing with the fixed-lug tool involves rotating the tool string a small amount, repeating the process until an increase in weight is observed. However, the depth and inclination of this well introduced significant pipe stretch and friction; this would mask the additional weight of the whipstock when observed using surface gauges, and the tool string windup would make it difficult to finely control the fixed-lug orientation. The availability of real-time data was invaluable in understanding what activities were occurring downhole during the operation.

The fishing bottomhole assembly (BHA) was run in the hole to just above the target depth of 7,520 m (24,672 ft) and oriented to match what was suspected to be the last high side of the whipstock. Once the depth was established, the systematic process of catching the whipstock dovetail slot with the lug could begin. The fishing tool was carefully rotated in increments of five degrees, as measured by the downhole sensors, before picking up and watching for overpull. Once overpull was observed from the downhole sensors, the operator pulled out of the hole confident that the objective had been achieved. Without the assistance of real-time data, this fishing job would have required days to perform. Instead, it took only a few hours.

Before attempting the window milling a second time, the operator agreed to perform a designated cleanout run to clear debris and ensure a smooth passage to the target depth. The cleanout tool string was equipped with sensors to measure tension, compression and bending moments. The real-time tension and compression data provided a useful measure of progress for the cleanout operation, and the high-resolution torque and drag data stored to memory were later used to provide an accurate model of the wellbore path. High local doglegs were detected, and engineering support modified the proposed whipstock BHA design to minimize the bending and compressive loads based on the new data.

Following the designated cleanout run, the whipstock system was deployed to perform the casing exit. The real-time data were used once again to improve milling-system performance.

Typically, it is necessary to take extreme caution when cutting a window at this depth because of the amount of uncertainty regarding weight and torque transferred. Indirect clues such as size and shape of the metallic cuttings when they arrive at the surface are the only way to tell if a job is progressing well. The smart intervention service enabled the operator to measure the milling parameters directly and provided early warning of pack-off conditions or milling dysfunction. The window in the 95/8-in. casing was completed in less than six hours.

The tools were then pulled out of hole, and gauging the mill confirmed that the operation had been completed successfully. The casing exit was completed with no NPT.

Real-time downhole data are a valuable tool for improving the outcome of wellbore interventions in challenging well profiles as they provide timely and accurate information to the operator. With this information an operator can not only correct problems quickly but monitor for early warning signs of impending problems and thereby avoid them altogether. Knowing exactly what is happening at the bottom of the hole reduces the risk of NPT, improves job efficiency and reduces the total cost of wellbore interventions.

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