Drilling in complex reservoirs with high resistivity, multiple fractures, and stick-slip vibrations can pose significant challenges for logging-while-drilling (LWD) operations. Conventional resistivity tools may not provide the required data quality and resolution, while wireline logging may add operational time and risk in highly deviated wells. To overcome these challenges, Baker Hughes offers its StarTrak™ ML imaging service, which delivers high-quality resistivity data and imaging in real time, enabling effective geosteering and geostopping decisions.
The challenge: logging high-resistivity formation in Colombia
An operator drilling exploratory wells in Colombia faced a complex formation characterized by high resistivity, multiple fractures, stick-slip vibrations, and a risk of washouts in some mudstone layers. The operation required high-density LWD data to improve geostopping decisions and avoid dropping angle in some intervals. Simultaneously, the operator wanted to avoid the rig time of separate wireline runs to collect the resistivity data in the highly deviated wells (with inclinations from 35° to 45°).
As an alternative to wireline, the operator considered placing a resistivity sensor in the bottomhole assembly (BHA). However, the resistivity tools of other service providers required lower rates of penetration (ROPs) to minimize vibrational effects and collect LWD data at the required density and depth of investigation (DOI), which added time and cost to the drilling operation.
The solution: StarTrak ML service for high-resolution resistivity and imaging
Baker Hughes proposed its StarTrak ML imaging service, which operates reliably in high-vibration drilling environments to generate multi-laterolog resistivity and high-resolution images at the required DOI. The StarTrak ML service uses a direct current/guard electrode sensor configuration to measure formation resistivity near the borehole, producing a high-resolution borehole image with full 360-degree coverage while drilling.
The StarTrak ML service provides high-quality resistivity data and imaging that identifies geomechanical features like drilling-induced fractures and borehole breakouts with greater sharpness and clarity than other tools. The service also determines structural dip and dip azimuth to optimize real-time wellbore placement in the most productive reservoir interval. By locating the sweet spot, the service helps avoid costly sidetracks and nonproductive zones, enhancing hydrocarbon recovery.
The StarTrak ML service is integrated in an AutoTrak™ or conventional motor-enabled BHA, which allows for easy deployment and retrieval. The service does not require any ROP reduction to capture data at the desired density, which reduces operational time and cost. The service also mitigates operating risks and ensures flawless execution through close cross-discipline collaboration between Baker Hughes and the operator.
The results: successful TD reached without wireline logging
The StarTrak ML service successfully delivered high-density resistivity data and imaging in real time, enabling the operator to make accurate geostopping decisions and reach target depth in multiple wells. The service drilled a total of 7,638 ft in 212 hours with no ROP reduction, saving time and cost compared to wireline logging or other LWD tools. The service also reduced operational risks by eliminating additional wireline logging runs and identifying potential wellbore stability issues.
The table below summarizes the key benefits of the StarTrak ML service for the operator:
| Benefit | Description |
|---|---|
| High-quality resistivity data and imaging | Collected high-resolution resistivity measurements and images in complex reservoir environment |
| Effective geosteering and geostopping | Optimized wellbore placement in the most productive reservoir interval and avoided dropping angle in some intervals |
| Time and cost savings | Reduced operational time while eliminating additional wireline logging runs and maintaining high ROP |
| Risk mitigation | Mitigated operating risks and ensured flawless execution through close cross-discipline collaboration |