Autonomous Interpretation Methods of Ultrasonic Data Through Machine Learning Facilitates Novel and Reliable Casing Annulus Characterization

Abstract

Ultrasonic and sonic logs are increasingly used to evaluate the quality of cement placement in the annulus behind the pipe and its potential to perform as a barrier. Wireline logs are carried out in widely varying conditions and attempt to evaluate a variety of cement formulations in the annulus. The annulus geometry is complex due to pipe standoff and often affects the behavior (properties) of the cement. The transformation of ultrasonic data to meaningful cement evaluation is also a complex task and requires expertise to ensure the processing is correctly carried out as well interpreted correctly. Cement formulations can vary from heavy weight cement to ultralight foamed cements. The ultrasonic log-based evaluation, using legacy practices, works well for cements that are well behaved and well bonded to casing. In such cases, a lightweight cement and heavyweight cement, when bonded, can be easily discriminated from gas or liquid (mud) through simple quantitative thresholds resulting in a Solid(S) - Liquid(L) - Gas(G) map. However, ultralight and foamed cements may overlap with mud in quantitative terms. Cements may debond from casing with a gap (that is either wet or dry), resulting in a very complex log response that may not be amenable to simple threshold-based discrimination of S-L-G. Cement sheath evaluation and the inference of the cement sheath to serve as a barrier is complex. It is therefore imperative that adequate processes mitigate errors in processing and interpretation and bring in reliability and consistency. Processing inconsistencies are caused when we are unable to correctly characterize the borehole properties either due to suboptimal measurements or assumptions of the borehole environment. Experts can and do recognize inconsistencies in processing and can advise appropriate resolution to ensure correct processing. The same decision-making criteria that experts follow can be implemented through autonomous workflows. The ability for software to autocorrect is not only possible but significantly enables the reliability of the product for wellsite decisions. In complex situations of debonded cements and ultralight cements, we may need to approach the interpretation from a data behavior-based approach, which can be explained by physics and modeling or through observations in the field by experts. This leads a novel seven-class annulus characterization [5S-L-G] which we expect will bring improved clarity on the annulus behavior. We explain the rationale for such an approach by providing a catalog of log response for the seven classes. In addition, we introduce the ability to carry out such analysis autonomously though machine learning. Such machine learning algorithms are best carried out after ensuring the data is correctly processed. We demonstrate the capability through a few field examples. The ability to emulate an "expert" through software can lead to an ability to autonomously correct processing inconsistencies prior to an autonomous interpretation, thereby significantly enhancing the reliability and consistency of cement evaluation, ruling out issues related to subjectivity, training, and competency.

Assisted Cement Log Interpretation Using Machine Learning

“…In particular, note that the interpretation schema includes MAWCem/MADCem codes representing various types of microannuligaps between casing and cement ranging from a few µm to hundreds of µm (Jutten and Hayman 1993, Kalyanraman et al 2017, Issabekov et al 2017) -and the FORMCEM codes representing combinations of cement and formation, typically where a creeping formation has closed in on the casing through the gaps in cement coverage (see e.g. Kalyanraman et al 2021 for an example). Furthermore, for each log, the dataset contains essential external information that is not provided in the original log file, such as:…”

“…These rotating tools comprise an angled transmitter that excites flexural waves on the casing and two angled receivers that estimate these waves' attenuation. Such estimates can be combined with pulse-echo-based estimates of acoustic impedance to help differentiate between higherimpedance liquids and lower-impedance solids (van Kuijk et al 2005, Kalyanraman et al 2021.…”

“…Furthermore, other related work has been reported: Kalyanraman et al (2021) discussed how errors in the processing of ultrasonic pulse-echo and pitch-catch measurements can be corrected (either manually or via ML) to facilitate a more robust and detailed characterization of the annular materials behind the casing prior to interpretation. Imrie (2021) developed an algorithm to distinguish between good cement coverage, partially good bonding quality cement coverage, and free pipe using CNNs on acoustic impedance maps.…”