Through-Tubing Casing Inspection for Well Integrity Evaluation Using Physics-Driven Machine- Learning Nonlinear Correction

Multiple casing inspection is a major part of well integrity evaluation during the entire well life cycle. One important aspect is casing deformation that can be prevalent in fields with formation swelling or subsidence, tectonic activity, salt creep, corrosion-induced or well completion defects. Worldwide experience shows that deformation will generally occur on the outer casing of the well. Therefore, a practical method that allows inspection of these tubulars is essential to evaluate and ensure well integrity. The challenge with existing logging technologies for pipe inspection, including calipers, ultrasonic, magnetic tools, and cameras, is the presence of tubing in the well that is costly to pull in order to evaluate the outer casing. Although there are available technologies that provide circumferential average thickness measurements for multiple concentric pipes, they cannot provide a casing deformation analysis. In order to address this longstanding challenge, this research work introduces a new tool using magnetic technology that enables measurement of casing deformation behind completion tubing, avoiding the cost of pulling tubing for surveillance purposes. In addition, this technology provides the capability to evaluate the tubing eccentricity inside the casing. Through-tubing casing inspection presents challenges in mainly two areas; the first is the tubing shielding effect. On average, only 20% of the magnetic flux density reaches the casing area behind the tubing. Secondly, when the tubing is eccentered inside the casing, it generates substantial nonlinear interference. This later distorts the casing measurement signal by creating a nonlinear effect on its response. A novel solution is developed to generate a unique compressed-and-focused magnetic field in order to increase the flux density in the casing area by two to three times, providing an increased signal-to-noise ratio. It also incorporates an array of magnetic sensors to measure the magnetic flux density distributions azimuthally around the tool. To improve the response, a physics-driven machine-learning method is tailored for the dimensionality reduction on the parameters’ domain, reducing the dimensions from 5D to 3D. An optimized Gaussian processes regression method is developed to process the raw logging data. In conjunction with casing inspection, the solution can quantitatively estimate the tubing eccentricity allowing for the removal of its nonlinear effects, including the extreme scenario of tubing touching the casing. This provides an estimated 5% deformation ratio accuracy for casing diameters up to 13.375 in. This technology can be combined with other single or multibarrier inspection logging tools, such as multifinger calipers and multipipe thickness log tools, respectively. This provides an integrated solution with complete well integrity evaluation. The tool’s performance has been validated in the lab as well as in the field, spanning different well conditions and casing/tubing combinations. Examples of applications in the oil and gas fields include through-tubing well integrity monitoring for production, injection, or gas storage wells, free-point logging, pipe eccentricity for plug and abandon operations, and tubing clamp orientation detection.