Research on the Spatial Distribution of Drilling Resistance Characteristics in Mahu Shale Formations
Authors: Yang Zhehua , Mu Zongjie , Tian Shouceng , Shi Jiangang , Popov Yury 
Published: August 2024

ABSTRACT:
To provide theoretical support for optimizing the speed-up technology measures of drilling in the Mahu shale formations, this paper explores and establishes a model for predicting rock drillability by comprehensively analyzing and applying different techniques and methods, predicting the spatial distribution pattern of drillability extremes in deep rocks.Firstly, based on rock mechanics tests and logging data, a rock drillability evaluation model based on single-factor and multi-factor logging parameters was established. Factor assessment analysis was conducted to obtain logging parameters that significantly affect drillability extremes, and the accuracy of the model was verified, clarifying the relationship between rock drillability and logging parameters. Secondly, based on the multi-factor drillability prediction model, the drillability extremes were predicted longitudinally, and the distribution pattern of drillability in specific strata depths was predicted and analyzed horizontally using the Kriging interpolation method, resulting in the spatial distribution pattern of drillability in the region.

1. INTRODUCTION
The Fengcheng Formation shales in the Mahu area are primarily argillaceous mud shales, characterized by complex lithology and great burial depth, resulting in poor adaptability to current horizontal well drilling acceleration techniques and significant impact on drilling efficiency (Lu et al., 2019). The primary reason is the insufficient match between the selected drill bits and the formation lithology. To address this issue, further research on the formation properties of the Mahu block in the Xinjiang oil field is required. Rock drillability is the most direct parameter reflecting the ease or difficulty of drilling into a formation and is one of the important bases for drill bit selection during each drilling process (Qian et al., 2020; Zhen et al., 2012). Accurate prediction of the drillability of deep formation rocks is crucial for studying the spatial distribution of parameters, guiding drill bit selection, and optimizing drilling parameters to provide references for adopting reasonable wellbore stabilization measures (Jinwang, 2016).
To accurately evaluate the rock's resistance to drilling, mathematical models based on logging data are commonly used internationally. These methods are mainly divided into three categories:(1) Regression analysis combining indoor core analysis and logging parameters is used to establish a calculation model for rock mechanical strength (Nes et al., 1998; Zausa & Santarelli, 1995).(2) Rock strength parameters are indirectly derived by analyzing rock cuttings obtained during drilling (Ringstad et al., 1998; Zausa et al., 1997). (3) A mathematical model for rock mechanical strength is constructed using multi-parameter logging data through neural network technology (Hamada & Elshafei, 2009; Zhang et al., 2004). Although these methods have improved the accuracy of rock mechanical property assessment to some extent, the analysis of the lateral variation of formation rock mechanical strength is still limited by various factors, such as the complexity of geological structures and the quality and frequency band range of seismic data. These factors can lead to significant errors in the prediction results (Geng et al., 2014; Zou et al., 2004).