Deep-Learning Techniques Classify Cuttings Volume of Shale Shakers
Authors: Carpenter Chris 
Published: April 2020

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 194084, “Classifying Cutting Volume at Shale Shakers in Real Time by Video Streaming Using Deep-Learning Techniques,” by Xunsheng Du, Yuchen Jin, and Xuqing Wu, University of Houston, et al., prepared for the 2019 SPE/IADC International Drilling Conference and Exhibition, The Hague, 5–7 March. The paper has been peer approved and is scheduled for publication in SPE Drilling & Completion.
A real-time deep-learning model is proposed to classify the volume of cuttings from a shale shaker on an offshore drilling rig by analyzing the real-time monitoring video stream. As opposed to the traditional, time-consuming video-analytics method, the proposed model can implement a real-time classification and achieve remarkable accuracy. The approach is composed of three modules. Compared with results manually labeled by engineers, the model can achieve highly accurate results in real time without dropping frames.
Introduction
A complete work flow already exists to guide the maintenance and cleaning of the borehole for many oil and gas companies. A well-formulated work flow helps support well integrity and reduce drilling risks and costs. One traditional method needs human observation of cuttings at the shale shaker and a hydraulic and torque-and-drag model; the operation includes a number of cleanup cycles. This continuous manual monitoring of the cuttings volume at the shale shaker becomes the bottleneck of the traditional work flow and is unable to provide a consistent evaluation of the hole-cleaning condition because the human labor cannot be available consistently, and the torque-and-drag operation is discrete, containing a break between two cycles.
Most of the previous work used image-analysis techniques to perform quantitative analyses on the cuttings volume. The traditional image-processing approach requires significant work on feature engineering. Because the raw data are usually noisy with missing components, pre- processing and augmenting the data play an important role in making the learning model more efficient and productive. The deep-learning framework, on the other hand, automatically discovers the representations needed for feature detection or classification from raw data. It can help overcome the difficulties in setting up and monitoring devices in a harsh environment, and the data-acquisition requirement for a cuttings-volume-monitoring system at the offshore rig might be relaxed.
The objective of this study is to verify the feasibility of building a real-time, automatic cuttings-volume-monitoring sys-tem on a remote site with a limited data-transmission bandwidth. The minimum data-acquisition hardware requirement includes the following:
Single uncalibrated charged-coupled-device camera
Inconsistent lighting sources
Low-bit-rate transmission
Image-processing unit without graphics-processing-unit support (e.g., a laptop)
A deep neural network (DNN) is adopted to perform the image processing and classification on cuttings volumes from a shale shaker at a remote rig site. Specifically, the convolutional neural networks are implemented as feature extractors and classifiers in the described model. The main contributions of this study can be summarized as follows:
A deep-learning framework that can classify the volume of cuttings in real time
A real-time video analysis system that requires minimum hardware setup efforts, capable of processing low-resolution images
An object-detection work flow to detect automatically the region covered by cuttings
A multithread video encoder/decoder implemented to improve real-time video-streaming processing