Key Takeaways & Executive Findings
- •• An improved 3D geomechanical modeling method integrates multi-source data and three LC-related sensitivity factors (fracture characteristics, rock brittleness, in-situ stress) for pre-drilling lost circulation risk prediction. • A quantitative risk assessment model combines the analytic hierarchy process (AHP) and entropy weight method (EWM) to assign weights, overcoming limitations of arbitrary factor selection and subjective weighting. • The model enables digital visualization of regional risk zones, validated against field-identified LC zones with an average relative error of 19.08%, confirming its reliability. • The method provides practical guidance for mitigating lost circulation risks and optimizing drilling program designs in fractured formations.
Abstract
Due to complex geological structures and a narrow safe mud density window, offshore fractured formations frequently encounter severe lost circulation (LC) during drilling, significantly hindering oil and gas exploration and development. Predicting LC risks enables the targeted implementation of mitigation strategies, thereby reducing the frequency of such incidents. To address the limitations of existing 3D geomechanical modeling in predicting LC, such as arbitrary factor selection, subjective weight assignment, and the inability to achieve pre-drilling prediction along the entire well section, an improved prediction method is proposed. This method integrates multi-source data and incorporates three LC-related sensitivity factors: fracture characteristics, rock brittleness, and in-situ stress conditions. A quantitative risk assessment model for LC is developed by combining the subjective analytic hierarchy process with the objective entropy weight method (EWM) to assign weights. Subsequently, 3D geomechanical modeling is applied to identify regional risk zones, enabling digital visualization for pre-drilling risk prediction. The developed 3D LC risk prediction model was validated using actual LC incidents from drilled wells. Results were generally consistent with field-identified LC zones, with an average relative error of 19.08%, confirming its reliability. This method provides practical guidance for mitigating potential LC risks and optimizing drilling program designs in fractured formations.
1. Introduction
Fractured reservoirs have become one of the important fields in oil and gas exploration and development, accounting for over 60% of globally proven oil and gas reserves [1]. Fractured formations are extensively distributed both onshore and offshore, with a variety of geological settings, such as micro-fractured deep tight conglomerate reservoirs in the Mahu Sag, Junggar Basin, China [2]; highly faulted and fractured Point Pleasant-Utica shale formations in the Appalachian Basin, USA [3]; highly fractured vuggy limestone and dolomite formations in offshore Abu Dhabi, UAE [4]; and Utica shale formations with natural fracture networks in the St. Lawrence sedimentary platform, Canada [5]. However, these formations tend to exhibit low pressure-bearing capacity due to the development of complex geological structures such as micro-fractures and macro-faults. As a result, the safe mud density window during drilling operations becomes a narrower and less discernible range [6], significantly increasing the risk of severe lost circulation (LC) events.
LC typically occurs in the forms of vugular, matrix seepage, and fractured losses. Although the first two types of losses may be concerning, LC caused by natural fractures and induced fractures in fractured formations accounts for over 90% of operators' lost returns expenditures [7]. LC can result in increased non-productive time, loss of drilling fluid, consumption of LC materials, and reservoir damage. It may also trigger a series of complex incidents, such as borehole collapse, pipe sticking, blowout, and even borehole abandonment. These issues contribute to additional drilling costs and severely restrict the efficient exploration and development of oil and gas resources in fractured formations [8]. According to statistics [9], severe LC events occur in over 30% of wells drilled in the Middle Eastern carbonate fractured reservoirs, with LC-related time accounting for more than 50% of the total drilling time. At China National Petroleum Corporation, downtime...
Loading authentic research manuscript (Pages 1–5)...
Jinfa Zhang, Yongcun Feng, Sijia Ma, Zhijuan Hao, Bing He, Jingyi Wei, Jingen Deng (2025). Prediction of lost circulation risk in fractured formations based on 3D geomechanical modeling. SinoTechIntel Verified Research. https://doi.org/10.1016/j.ijmst.2025.08.008
Research & Educational Purpose Only:The translations, structured abstracts, analytical annotations, and data reports provided by SinoTechIntel are intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoTechIntel claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
What is the main objective of the study?
The main objective is to develop an improved 3D geomechanical modeling method for predicting lost circulation risk in fractured formations, addressing limitations of existing methods such as arbitrary factor selection and subjective weight assignment.
How does the proposed method improve lost circulation risk prediction?
The method integrates multi-source data and incorporates three LC-related sensitivity factors (fracture characteristics, rock brittleness, in-situ stress conditions). It uses a combination of analytic hierarchy process (AHP) and entropy weight method (EWM) for objective weight assignment, and applies 3D geomechanical modeling for digital visualization and pre-drilling risk prediction.
What are the key factors considered in the risk assessment model?
The key factors are fracture characteristics, rock brittleness, and in-situ stress conditions. These are integrated into a quantitative risk assessment model with weights determined by AHP and EWM.
How was the model validated?
The model was validated using actual lost circulation incidents from drilled wells. The predicted risk zones were generally consistent with field-identified LC zones, with an average relative error of 19.08%, confirming its reliability.
What practical applications does the method have?
The method provides practical guidance for mitigating potential lost circulation risks and optimizing drilling program designs in fractured formations, helping to reduce non-productive time and drilling costs.
Related Technical Papers & Translations
A Novel Approach for Enhanced Brain Tumor Segmentation Using Multimodal MRI and Deep Learning
Brain tumor segmentation from multimodal MRI is crucial for diagnosis and treatment planning. In this study, we propose a novel deep learning framework that integrates structural and functional imaging modalities to improve segmentation accuracy. Our method employs a multi-scale attention mechanism and a hybrid loss function to handle class imbalance and boundary ambiguity. Evaluated on the BraTS benchmark, our approach achieves state-of-the-art performance, with Dice scores of 0.91, 0.87, and 0.84 for whole tumor, core, and enhancing tumor, respectively. Furthermore, we demonstrate the generalizability of our model across different scanners and protocols. Our findings suggest that the proposed method can significantly aid clinical decision-making and surgical planning.
Investigation of coupled acoustic and electrical responses and early warning approaches during re-loading of damaged coal
Initial damage from engineering disturbances in deep coal mining degrades mechanical properties and heightens dynamic-hazard risks, challenging conventional monitoring. This study probes the coupled acoustic-electrical responses of initially damaged coal under reloading and develops a multi-parameter, multi-level dynamic integrated early-warning model. Using a true-triaxial Split Hopkinson Pressure Bar (SHPB) system, we prepared specimens with graded damage by varying static deviatoric stresses and dynamic impacts. Uniaxial compression reloading was conducted with synchronous acoustic emission (AE) and resistivity monitoring. Joint time-domain responses of force, acoustics, and electricity delineated distinct loading stages. Time-frequency features were extracted via Fourier and wavelet transforms; crack architecture was quantified by 3D AE localization and fractal-dimension analysis. Initial damage markedly reduced load-bearing capacity. Resistivity decreased sharply with increasing deviatoric stress, while cumulative AE counts increased strongly. The AE spectrum evolved from bimodal to broadband with low- and high-frequency enhancement. The resistivity spectrum showed progressive bandwidth broadening, energy amplification, and high-frequency advancement. The AE spatial fractal dimension rose significantly during compaction. An integrated warning system combining multiscale entropy fusion, Temporal Convolutional Network (TCN)-Transformer forecasting, recurrence-network analysis, and a Bayesian framework yielded a 28.4 s lead time, offering a theoretical basis and technical pathway for intelligent prevention of dynamic hazards.
Influence of aggregate particle size on fracture behavior and energy evolution of cemented rockfill in the post-peak stage
Cemented rockfill (CRF) combines structural support with sustainable reuse of coal-derived solid waste. This study integrates digital image correlation, acoustic emission monitoring, and finite–discrete element simulations to investigate mechanical behavior, fracture development, and energy evolution of CRF containing 54% aggregate content with three grain-size distributions (5–10, 10–20, and 20–30 mm). Results indicate finer aggregates raise compressive strength and elastic modulus, and increase post-peak softening and residual stiffness. Fracture patterns transition from dominantly unidirectional failure in coarse specimens to pronounced X-shaped conjugate shear in fine specimens, with cracks initiating at boundaries and propagating inward. The proportion of failed joints at comparable strains decreases markedly with finer gradation, reflecting a more homogeneous crack network that enhances post-peak load retention and produces frequent minor stress fluctuations. Energy analyses reveal a coarse > medium > fine ordering in cumulative dissipation; however, finer aggregates delay rapid kinetic and dissipative energy release, promoting slower energy redistribution and improved load resistance. These findings quantify how aggregate gradation controls deformational mechanisms, crack topology, and energy partitioning, and provide design guidance for optimizing aggregate size and cementitious composition to enhance ductility, energy absorption, and structural reliability of CRF in underground engineering.