Subsidence Model Research Articles (Page 1)

Abstract Land subsidence driven by groundwater exploitation poses a critical threat under future water stress from population growth, urbanization, and economic expansion. While the relationship between groundwater level (GWL) fluctuations and land subsidence has been studied, limited research has explored their co‐evolution under future development, climate change, and groundwater management aimed at stabilizing or reversing GWL decline. The long‐term effects of large‐scale water transfer projects, such as China's South‐to‐North Water Diversion (SNWD) project, on subsidence remain unclear despite their role in reducing extraction pressures and recovering groundwater level. This study develops a coupled groundwater flow and land subsidence model for a key city in the SNWD's middle route, incorporating water demand projections (from Shared Socioeconomic Pathways, SSPs), climate change scenarios (from CMIP6), and water diversion strategies. Results indicate that future (until 2050) GWL changes and subsidence are primarily driven by water demand (over 50%) and water diversion (up to 45.3%), with climate change having a minor effect (under 18.6%). Over the period extending from 2020 to 2050, subsidence recovery could reach 56.8 mm (averagely 1.9 mm/yr) with reduced demand and increased diversion. However, in a worst‐case scenario characterized by rising demand and absence of diversion optimization, subsidence could worsen by up to 439.9 mm (averagely 14.7 mm/yr). Water diversion could be 15 times more effective in mitigating subsidence under higher water‐stress conditions, while prohibiting deep groundwater extraction for agriculture could lead to a 4.4‐fold improvement in GWL recovery and subsidence mitigation. This study highlights the role of technology, policy, and optimized water diversion in managing GWL and mitigating subsidence under future uncertainties.

Interferometric Synthetic Aperture Radar (InSAR) technology is crucial for large-scale land subsidence analysis in cultivated areas within hilly and mountainous regions. Accurate prediction of this subsidence is of significant importance for agricultural resource management and planning. Addressing the limitations of existing subsidence prediction methods in terms of accuracy and model selection, this paper proposes a deep neural network prediction model based on Variational Mode Decomposition (VMD) and the Snake Optimizer (SO), termed VMD-SO-CNN-LSTM-MATT. VMD decomposes complex subsidence signals into stable intrinsic components, improving input data quality. The SO algorithm is introduced to globally optimize model parameters, preventing local optima and enhancing prediction accuracy. This model utilizes time–series subsidence data extracted via the SBAS-InSAR technique as input. Initially, the original sequence is decomposed into multiple intrinsic mode functions (IMFs) using VMD. Subsequently, a CNN-LSTM network incorporating a Multi-Head Attention mechanism (MATT) is employed to model and predict each component. Concurrently, the SO algorithm performs global optimization of the model hyperparameters. Experimental results demonstrate that the proposed model significantly outperforms comparative models (traditional Long Short-Term Memory (LSTM) neural network, VMD-CNN-LSTM-MATT, and Sparrow Search Algorithm (SSA)-optimized CNN-LSTM) across key metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Specifically, the reductions achieved are minimum improvements of 29.85% for MAE, 8.42% for RMSE, and 33.69% for MAPE. This model effectively enhances the prediction accuracy of land subsidence in cultivated hilly and mountainous areas, validating its high reliability and practicality for subsidence monitoring and prediction tasks.

Study DesignRetrospective study.ObjectiveTo develop and validate a predictive model for cage subsidence (CS) after midline lumbar interbody fusion (MIDLIF) with cortical bone trajectory (CBT) screws.MethodsThis retrospective two-center study included patients diagnosed with lumbar degenerative disorders undergoing MIDLIF between January 2018 and October 2023 at two independent hospitals under identical eligibility criteria and variable definitions. Patients were stratified into CS and non-CS groups according to postoperative outcomes. Variables with P < 0.1 in the univariate analysis were subsequently included in multivariate logistic regression to determine independent predictors. Bone mineral density (BMD) was indirectly evaluated using endplate bone quality (EBQ) scores from MRI and Hounsfield units (HU) measurements from CT scans. Inter-rater reliability of EBQ was reported using the intraclass correlation coefficient (ICC) with 95% CIs. The model's performance was assessed using ROC analysis, calibration curves, and decision curve analysis (DCA).ResultsAcross both centers, 316 patients were included, of whom 71 (22.5%) developed CS (development center: 48/216, 22.2%; external center: 23/100, 23.0%). Elevated BMI, higher EBQ scores, lower HU values, and reduced preoperative disc height were found to be independent predictors. The prediction model exhibited favorable discriminative ability, with AUCs of 0.924 in the training set and 0.884 in the internal validation set, and it maintained performance in a geographically external cohort (AUC = 0.842). Calibration curves demonstrated good agreement between predicted and observed outcomes, and DCA indicated strong clinical applicability. Although lower than in the training and internal validation sets, external net benefit stayed positive across a broad clinical threshold range and, for most thresholds, exceeded treat-none and treat-all. EBQ inter-rater reliability (ICC, 95% CIs) was 0.960 (0.945-0.971), 0.940 (0.902-0.964), and 0.920 (0.881-0.946) in the training, internal validation, and external cohorts, respectively. In addition, the nomogram was developed into an online calculator that visually displays the predicted probability of CS following MIDLIF.ConclusionsThe developed nomogram serves as a practical and reliable means to predict the risk of cage subsidence in patients undergoing MIDLIF. An online risk calculator based on this model further enhances its clinical utility, providing clinicians with a valuable reference for tailoring surgical strategies and improving perioperative decision-making.

With the gradual extension of global coal mining to the deep, the problem of surface subsidence caused by repeated mining of multiple coal seams has attracted much attention. In this paper, the methods of theoretical analysis, numerical simulation and field monitoring are used to study the settlement model and settlement law under the condition of repeated mining of multiple coal seams, and the engineering application is carried out. Through theoretical analysis, it is concluded that there is a linear relationship between the amount of broken expansion of overlying rock mass, the buried depth of coal seam, the thickness of coal seam and the maximum subsidence value of surface. The opening size of surface subsidence basin in multi-coal seam mining is still linear with the mining depth of coal seam. Based on the probability integral method, fully considering the factors such as rock mass expansion and separation caused by repeated mining of unequal thickness and multiple coal seams, the prediction model of surface subsidence caused by repeated mining of multiple coal seams is constructed, and the calculation method and calculation formula of surface subsidence coefficient caused by repeated mining of multiple coal seams are given. The law of overlying strata and surface subsidence caused by repeated mining of multiple coal seams is analyzed by numerical simulation. The results show that the trend of surface subsidence is similar to that of repeated mining (second-layer coal) in multi-coal seam mining area. The subsidence curve is symmetrically distributed in the early stage of mining and asymmetrically distributed in the later stage. The maximum value of surface subsidence is located on the side of open-off cut. The numerical simulation results are highly similar to the calculation results of ' prediction model of surface subsidence caused by repeated mining of multi-coal seam '. The surface subsidence prediction model constructed in this paper is used to predict the coal mine site. The prediction results are compared with the field measurement results. The maximum relative error value of the working face tendency is 5.7%, and the average relative error value is 2.9%. Along the strike of the working face, the maximum relative error value is 6.7%, and the average relative error value is 5%. The overall prediction error is small, which verifies the rationality of the prediction model of the surface subsidence law of multi-coal seam repeated mining.

Intensive groundwater extraction and a severe 2021 drought have worsened land subsidence in Taiwan's Choshui Delta, highlighting the need for effective predictive modeling to guide mitigation. In this study, we develop a machine learning framework for subsidence analysis using electricity consumption data from pumping wells as a proxy for groundwater extraction. A long short-term memory (LSTM) neural network is trained to reconstruct missing subsidence records and forecast subsidence trends, while an artificial neural network links well electricity usage to groundwater level fluctuations. Using these tools, we identify groundwater-level decline from pumping as a key driver of subsidence. The LSTM model achieves high accuracy in reproducing historical subsidence and provides reliable predictions of subsidence behavior. Scenario simulations indicate that reducing groundwater pumping, simulated by lowering well electricity use, allows groundwater levels to recover and significantly slows the rate of land subsidence. To assess the effectiveness of pumping reduction strategies, two artificial scenarios were simulated. The average subsidence rate at the Xiutan Elementary School multi-layer compression monitoring well (MLCW) decreased from 2.23 cm/year (observed) to 1.94 cm/year in first scenario and 1.34 cm/year in second scenario, demonstrating the potential of groundwater control in mitigating land subsidence. These findings underscore the importance of integrating groundwater-use indicators into subsidence models and demonstrate that curtailing groundwater extraction can effectively mitigate land subsidence in vulnerable deltaic regions.

The simulation of compressible delay interbed is an important component of ground subsidence modeling. Currently, the most widely used groundwater simulation software, MODFLOW, has two modules: SUB and CSUB. While both can simulate compressible delay interbed using the one-dimensional head diffusion equation, they differ in approach. The SUB module relies on the principle of head change, while the CSUB module is based on the principle of geostress variation, addressing the shortcomings of the SUB module when simulating ground subsidence in unconfined aquifers. When based on the principle of head change, the effective stress acting on the top and bottom of the interbed is the same, leading to symmetric vertical consolidation and using half-thickness discretization. In contrast, the CSUB module is based on geostress variation, the effective stress acting on the top and bottom of the interbed is not the same, which results in asymmetric consolidation and requires full-thickness discretization. With the same vertical discretization interval for the compressible delay interbed, the computational workload and memory requirements are approximately twice that of the SUB module. Based on the characteristic of linear distribution of geostress in the vertical direction of the compressible delay interbed, this paper proposes a half-thickness discretization format under the principle of geostress variation, which can significantly improve simulation efficiency and reduce memory requirements. To validate this approach, three cases were tested with different numbers of discretization units, different interbed thicknesses, different heads, and different vertical hydraulic conductivities. A comprehensive comparison was made between the half-thickness discretization format and the full-thickness discretization format of the CSUB module. The differences in computation time and memory usage between the two methods were then analyzed. The results show that the maximum difference in the interbed water release between the half-thickness method and the CSUB module is less than 0.4%, indicating good accuracy. Additionally, the half-thickness method reduced computation time by 46.2303% and memory usage by 13.6364% in the tested cases, demonstrating significant computational advantages. This study provides an efficient and feasible technical approach for large-scale, high-precision land subsidence modeling.

The “West–East Coal Transmission” project is an important resource allocation project in China, aiming to alleviate the shortage of coal resources in China’s economically developed eastern cities. Ensuring coal transportation railway safety is an important part for the smooth running of this project. However, China is a large coal mining country, and some railways inevitably pass through the coal mining influence areas, seriously threatening the structural health of the railways. The probability integral method (PIM) is an official prediction model for mining subsidence in China, while it is difficult to accurately predict the subsidence boundary of a mining area, and cannot scientifically and accurately evaluate the impact of coal mining on the deformation of coal transportation railways passing through the boundary, lacking effective guidance for railway protection. In response to this engineering issue, this paper analyzes the influence of PIM’s parameters change on the prediction results, based on which, the probability integral method modified model (PIM-MM) is established, the decreasing step fruit flies optimization algorithm (DS-FOA) used for parameters inversion is also proposed, which realizes the accurate subsidence prediction for the boundary of a mining area. At the same time, with the help of ground observation technique (SBAS-InSAR), the surface subsidence data of the study area was obtained, and the influence factors of the surface subsidence were analyzed, realizing the efficient and real-time monitoring of railway deformation. It can effectively guide the coal mining with the goal of railway protection, and has important social significance and engineering application value for the coordinated development of both.

During the maize middle and late periods, the soil between rows is soft and also involved with weeds and straw. When the plant protection robot (PPR) moves on the soil, there exists uncertain shear perturbation because of the shear action and pressure subsidence, leading to the difficulty of the controller design. In this work, we propose an adaptive path tracking control (PTC) considering disturbances for the PPR. The disturbance of PRR in contact with soil is first revealed according to Bekker pressure subsidence and Janosi shear models, through which the plant model of PPR system is established. Then, we propose an adaptive fixed-time sliding mode (AFTSM)-based PTC to achieve excellent path tracking performance, where an extreme learning machine (ELM) estimator is developed, releasing the requirement for bound derivations in the control design. Using the fixed-time control and the ELM techniques in the proposed control, a remarkable control performance is well ensured, i.e., high-accuracy tracking, fast convergence, and excellent robustness. Experimental studies on a PPR are executed for demonstrating the validity and good performance of the designed controller.

Abstract The Pannonian Basin System, comprising multiple sub-basins, forms the broader geological framework of this study, which focuses specifically on the North Danube Basin. Geochronology, sediment accumulation patterns, and tectonic subsidence modeling were integrated to update the tectono-sedimentary history of the basin. A volcanogenic marker bed dated at 13.75 ± 0.14 Ma using 40 Ar/ 39 Ar method provides a key stratigraphic reference for the Badenian (Langhian/Serravallian) stage. The subsidence history indicates rapid tectonic activity during the lower Badenian (Langhian), upper Badenian (lower Serravallian), and Sarmatian (upper Serravallian) stages, transitioning to a more stable setting during the lower Pannonian (Tortonian). Vitrinite reflectance models reveal hydrocarbon maturation across all sub-basins, with variations in oil window depths attributed to differential heat flow near volcanic fields. This study also investigates the anomalously high sediment accumulation rates in continental back-arc basins, exemplified by the North Danube Basin. Graphical abstract

Mining-induced seismic events generate ground surface displacements that can significantly impact infrastructure. This study presents a geometric analysis of ground surface displacements caused by mining-induced tremors using remote sensing techniques, particularly Differential Interferometric Synthetic Aperture Radar (DInSAR).The research focuses on two seismic events in the Legnica-Głogów Copper District (LGCD), Poland, examining displacement fields through SAR imagery from Sentinel-1 satellites. The Geometry of the displacement field was described with a function of stochastic medium displacement and Aviershin’s observation. The centre of gravity of the displacement field, its impact radius, and proportionality coefficient for vertical and horizontal displacement were determined. The results confirm the suitability of this model in describing mining-induced subsidence troughs, with high agreement between measured and calculated displacement values. Additionally, the study identifies systematic deviations in DInSAR - derived measurements, suggesting necessary corrections for improved accuracy. The findings contribute to a better understanding of ground surface deformation process and offer insights into refining predictive models for mining-induced subsidence.

Underground mining-induced surface subsidence adversely affects both the surface environment and the structures located above it. Accurately predicting the dynamic subsidence and deformation caused by underground mining is crucial when employing maintenance and remediation methods to mitigate these adverse effects, as it directly impacts the selection of maintenance strategies, timing, and volume assessments. In response to the limitations of traditional time function and parameter models in adapting to the dynamic changes of actual underground mining activities—resulting in low subsidence prediction accuracy—this paper presents an adaptive prediction model for dynamic subsidence supported by measured data and developed through programming. This model utilizes historically measured data on surface subsidence to derive optimal parameters for each historical period. By analyzing the trends in these parameters, it dynamically adjusts the parameter value for subsequent predictions, achieving high-precision prediction of the surface dynamic subsidence. Engineering case study results indicate significant variations in the optimal time function parameter values throughout the mining process. The estimated parameter values obtained through the extrapolative prediction method, supported by measured data, align closely with the optimal values. The average relative RMSE of predicted dynamic subsidence for each period is 4.3%, markedly lower than the 9.1% achieved by traditional prediction models. This enhancement significantly improves the accuracy of dynamic subsidence predictions due to underground mining and provides robust technical support for the maintenance and remediation of structures.

Time-Series models for ground subsidence and heave over permafrost in InSAR Processing: A comprehensive assessment and new improvement

SUMMARY Seismic source models that use an elastic relation between pressure decrease, compaction and stress change have been shown to successfully reproduce induced seismicity in producing natural gas reservoirs undergoing differential compaction. However, this elastic relation is inconsistent with observations of nonlinear reservoir compaction in the Groningen field. We utilize critical state mechanics theory to derive a 3-D stress–strain framework that is able to house 1-D nonlinear stress–strain relations typically used for subsidence models, without the need for recalibration of the subsidence model parameters. This is used to adapt the elastic thin sheet stress model that is currently in use as the state-of-the-art for seismicity predictions as part of the hazard and risk assessment of the Groningen gas field. The new thin sheet model has one additional model parameter that modulates the impact of inelastic deformation on fault loading, whilst keeping the intended function of the model calibration from the original elastic thin sheet model intact. The resulting elastic-viscoplastic thin sheet stress model is consistent with previously reported nonlinear rate-dependent reservoir compaction in Groningen found from inverting subsidence data and from rock deformation experiments. Our elastic-viscoplastic thin sheet stress model is able to predict ongoing stress increase, and therefore ongoing seismicity, in areas where pressure does not decrease anymore due to shut-in. A pseudo-prospective forecasting exercise indeed shows that the elastic-viscoplastic stress model performs better than the linear elastic stress model. This model addition ensures that the Groningen seismic source model is well suited for predicting seismicity in the post shut-in phase.

ABSTRACTSedimentary infill patterns in the Eastern Gobi Basin of southern Mongolia record a complex, polyphased history. Asynchronous timing and intensities of extensional tectonism during the Early Cretaceous fragmented the Eastern Gobi Basin into a series of sub‐basins within an extensional rift (horst‐graben) setting, which likely infilled penecontemporaneously to asynchronously. Of these sub‐basins, the north‐eastern Sainshand sub‐basin preserves a nearly continuous Lower Cretaceous syn‐rift succession. However, many outstanding uncertainties concerning intra‐sub‐basinal and inter‐sub‐basinal biostratigraphic correlations persist, including stratigraphic linkages locally at the Dzun Shakhai and Shine Usny Tolgod localities, regionally across the eastern Sainshand sub‐basin, along with the adjacent Zuunbayan and Unegt sub‐basins. This study confirms that Dzun Shakhai and Shine Usny Tolgod are hosted within a horst‐graben setting with sedimentary successions composed of locally sourced (para‐autochthonous to autochthonous) detritus. Facies analysis reveals a broad suite of evolving transitional depositional environments, including alluvial, fluvial and lacustrine environments. Basin infill initiated in a retrogradational setting (underfilled‐starved stage) that transitioned to an aggradational and a subsequent progradational setting (filled stage). Based on the identification of six syn‐rift sequence boundaries (SR1 to SR6), this study determined that this portion of the Sainshand sub‐basin fits a gradual subsidence model. Additionally, this study presents significant sedimentological evidence for: (i) the designation of a new member, the Ikh Ulaan Nuur Member of the Shinekhudag Formation; and (ii) the subdivision of the Khukhteeg Formation into an informal lower and upper member. These novel sedimentological data improve lithostratigraphic and palaeoenvironmental correlations across the Sainshand sub‐basin, with strengthened correlations to the adjacent Zuunbayan and Unegt sub‐basins and more peripheral linkages to the Erlian, Yingen and Songliao basins of north‐eastern China. These findings provide an important foundation for assessing the spatiotemporal distribution of syn‐rift fossil‐bearing units across the greater Eastern Gobi Basin and the North China Block.

Abstract The Shinarump and Gartra Members form the basal part of the Chinle Formation in western USA and are the deposits of a river system that flowed northwestward from the Ouachita Orogen to the Auld Lang Syne basin during the Late Triassic. Previous estimates of paleoslope for this river have been limited by low numbers of data points. This study, therefore, presents a dataset of 1133 cross-set height measurements to form the basis for paleoslope reconstructions, and as part of a facies analysis which additionally includes clast counts identifying a total of 13,584 clasts, grain-size analyses measuring 7400 grains and paleoflow analyses composed of a further 975 trough cross-sets. Lithofacies analyses describe the Shinarump and Gartra Members as the deposits of a braided river system and identify previously unrecognized antidune deposits at the Vermilion Cliffs, northern Arizona. This suggests that the sandy facies at the top of the Shinarump Member may be deposits of flash flood events. Grain-size and cross-set height analysis allow for estimates of paleoslope to be produced, which range from 9.6 × 10−5 to 4.3 × 10−4, with a median value ~2.5 × 10−4, on par with many modern continental scale rivers. These estimates predict that the upper surface of the Chinle basin was ~75–150 m above sea level on the Colorado Plateau at its time of deposition. To reveal the amount of subsidence necessary to accommodate the Chinle Formation, we tied a 2-D backstripping analysis to the calculated paleoslope reconstruction. The resulting basin accommodation distribution describes a low-magnitude (hundreds of meters), long-wavelength (&amp;gt;1000 km) deflection, fully compatible with characteristics of dynamic topography. The combination of subsidence analysis with an independent paleoelevation metric can be applied to other members of the Chinle Formation and may be useful in other similar contexts where dynamic topography is difficult to quantify.

In recent years, the prolonged exploitation of coal resources has led to the depletion of coal reserves in some mining areas, resulting in the closure of certain mines worldwide. After mine closures, the fractured rock masses in abandoned mine cavities undergo weathering and degradation due to factors such as stress and groundwater, leading to reduced strength. This change alters the stress distribution and load-bearing capacity of the fractured rock within the abandoned voids, resulting in secondary or multiple deformations on the surface, which pose significant potential threats to surface infrastructure and public safety. Research into the mechanisms, patterns, and predictive methods of secondary surface subsidence in closed mines is thus of great theoretical and practical significance. Based on a literature review and practical monitoring experience in closed mine sites, this study systematically examines and analyzes the current state of secondary surface subsidence monitoring methods, formation mechanisms, spatiotemporal distribution patterns, and prediction methods in closed mines, as well as existing challenges. Initially, we compare the advantages and limitations of conventional surface deformation monitoring techniques with remote sensing techniques, emphasizing the benefits and issues of using InSAR technology for monitoring surface subsidence in closed mines. Next, by reviewing extensive data, we analyze the formation mechanisms and spatiotemporal evolution of secondary surface subsidence in closed mines. Building on this analysis, we discuss numerical and analytical methods for predicting secondary surface subsidence mechanisms in closed mines, evaluating the strengths and weaknesses of each approach. Predictive models for surface subsidence and uplift phases in the longwall collapse method are presented based on the constitutive relationships of fractured rock masses. Finally, the study highlights that the mechanisms and patterns of surface subsidence in closed mines represent a highly complex physical–mechanical process involving geological mining environments, fractured rock structures, constitutive relations, deformation characteristics, hydro-mechanical interactions, and groundwater dynamics, underscoring the need for further in-depth research.

PREDICTIVE COMPUTER MODELING OF THE SURFACE SUBSIDENCE OF A SOLID WASTE STORAGE FACILITY AND GAS GENERATION UNDER UNSATURATED CONSOLIDATION CONDITIONS. RIVNE

Analysis of subsidence factors and modeling of susceptibility under coupled geohydrological conditions - A case study of Jiangsu Yangtze River section

The gob-side entry driving in deep mines with soft rock exhibits a complex deformation and instability mechanism. This complexity leads to challenges in roadway stability control which greatly affects the coal mine production succession and safe and efficient mining. This paper takes the gob-side entry in Liuzhuang Coal Mine as the background. By adopting the method of theoretical analysis, a dynamic model of the roof subsidence in the goaf is established. The calculation indicates that achieving the stable subsidence of the basic roof and the equilibrium of the lateral abutment stress within the goaf requires a minimum of 108.9 days, offering a theoretical foundation for selecting an optimal driving time for the gob-side entry. The control technologies and methods of gob-side entry through grouting modification and high-strength support are proposed. Enhancing the length of anchor ropes and the density of bolt (cable) support to improve the role of the roadway support components can be better utilized, so the role of the support components of the roadway can be better exerted. The method of grouting and the reinforcement of coal pillars can effectively improve the carrying capacity of coal pillars. The numerical simulation is used to analyze the deformation law of gob-side entry. The study reveals significant deformation in the coal pillar and substantial roof subsidence, highlighting that maintaining the stability of the coal pillar is crucial for ensuring roadway safety. Following the grouting process, the deformation of the coal pillar and roof subsidence decreased by 16.7% and 7.1%, respectively. This demonstrates that coal pillar grouting not only mitigates pillar deformation but also provides effective control over roof subsidence. This study offers a quantitative calculation method to ascertain the excavation time of gob-side entry, and suggests that the application of high-strength support and the practice of coal pillar grouting can effectively maintain the steadiness of gob-side entry in deep mines with soft rock.

When oil and gas wells become depleted and reservoir pressures decreases, the porous formation contracts due to poromechanics effects, causing significant stress redistribution and global deformation of the rock mass. In severe cases, there may be significant displacement of the surface (or ocean bottom), termed subsidence. Both surface and underground displacements are transmitted to well barrier elements, including wellheads, casing and cementing, potentially leading to hydrocarbon leaks. However, coupling geomechanical models of subsidence with well structures presents significant modeling and simulation challenges. Axisymmetric modeling of vertical wells subject to transversely isotropic subsidence strains is relatively straightforward and extensible to slightly deviated wells, but any other cenario requires costly and challenging tridimensional modeling. Therefore, although subsidence is a global problem, it is important to develop local coupling models. In this work, we present the axisymmetric modeling approach and discuss the challenges associated with applying boundary conditions for the general case of a deviated well in a fully triaxial strain state. Using a contracting material model for the rock, we were able to observe pipe loads such as axial and shear forces, as well as ovalization and external pressure, in the cemented casing. This opens a path toward 1D modeling of casing pipe, which is the traditional well casing design approach.