A Real-Time Indicator for the Evaluation of Hole Cleaning Efficiency

Abstract

Drilling operations are considered a major cost in the development phase of oil and gas wells, which places huge emphasis on drilling efficiency as a leading factor in cost reduction and optimization. Nonproductive time (NPT) incidents such as stuck pipes must be minimized by careful planning and close well monitoring to enhance drilling efficiency. One frequent and global cause of stuck pipes is the inefficient removal of formation cuttings from the wellbore while drilling, which is the focus of the developed real-time model. Poor hole cleaning is also a major contributor to other NPTs such as loss of circulation and formation fracturing, which can be induced due to the high equivalent circulating density (ECD) caused by the presence of excess cuttings. Insufficient hole cleaning, if not tackled properly and in a timely manner, can lead to NPT incidents and consequently increases the drilling cost significantly. In addition to NPT reduction, proper hole cleaning can increase the rate of penetration (ROP) and reduce torque and drag. A tool that provides a real-time indication of the hole cleaning efficiency is therefore very valuable to have better control of the hole conditions, especially in critical wells. Such indicators will provide continuous monitoring of the wellbore and will allow immediate intervention for detected abnormalities. There are several indexes that evaluate hole cleaning efficiency while drilling. The index that will be the core of the developed model is the Carrying Capacity Index (CCI), which is defined as the ability of a mud system to circulate the cuttings to the surface. The index is influenced mainly by the drilling fluid properties and flow hydraulics, which are both controllable factors that allow the rig crew to adjust on location to ensure sufficient cleaning of the cuttings. The developed system automatically calculates the CCI in real-time. Thousands of raw values are generated from rig sensors continuously, which makes the calculations nearly impossible for a human to perform. The model is developed to take in the raw data and use it as an input in conjunction with some well details, such as hole size and casing size, to generate the CCI. This system takes us one step closer toward the ultimate goal of having an integrated and fully automated hole cleaning evaluation and intervention tool that does not require any human involvement.

A Novel Model for the Real-Time Evaluation of Hole-Cleaning Conditions with Case Studies

“…In addition, they modified the cutting carrying index by including cutting rise velocity with annular velocity and then applied the CCI to real-time data to monitor and evaluate the hole-cleaning efficiency and thereby optimize well and rig performance. Alawami et al (2020) applied the hole-cleaning carrying-capacity index to real-time data to monitor and evaluate the hole-cleaning performance of drilled wells by using offline real-time data [10]. Mahmoud et al (2020) modified the CCI to make it applicable in cleaning deviated hole sections.…”

“…Cutting accumulation and shale caving will lead to difficult tripping operations resulting from pipe sticking [8]. In fact, approximately 33% of stuck pipe incidents in deviated and horizontal directions are caused by inadequate downhole cleaning while drilling, making it a significant contributor to these types of events [4,9,10]. Moreover, effective hole cleaning is a crucial aspect of maximizing production rates in a well, as it can significantly impact the success of subsequent techniques such as acidizing and CO 2 injection.…”

Estimating the Critical Velocity of the Incipient Motion of Particles on the Cuttings Bed Surface: An Experimental and Theoretical Analysis

“…When circulation is restarted, the cutting particles on the cuttings bed surface are mobilized and transported to the wellhead by the drilling fluid. The inadequate removal of the cuttings bed can lead to hole cleaning problems such as high rotary torque, a stuck pipe, formation breakdown, a slow rate of penetration, and a loss of circulation [4][5][6][7]. Therefore, studying the incipient motion law of cuttings beds after the drilling fluid is recycled can guide field engineering practices in determining a reasonable pump displacement to improve hole-cleaning efficiency.…”

Applying Machine Learning to Predict the Rate of Penetration for Geothermal Drilling Located in the Utah FORGE Site

“…A study by [4] established that the ROP of a roller cone bit, under perfect hole cleaning conditions, is proportional to the rotary speed and the bit weight squared, and inversely proportional to the bit diameter squared. However, these conditions are not necessarily met [5]. Thus, poor hole cleaning scenarios cause an increase in ROP due to the increasing WOB of the hook load adjustment.…”