Geomechanical Evaluation Enabled Successful Stimulation of a HPHT Tight Gas Reservoir in Western China

Zhang, Hui; Qiu, Kaibin; Fuller, John; Yin, Guangzhi; Fang, Yuan; Chen, Sheng

doi:10.2523/iptc-17770-ms

Cited by 8 publications

(5 citation statements)

References 53 publications

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“…It includes elastic properties, rock strength data, and geostresses (Plumb et al 2000, Ali et al 2003. Some of the work on 1D MEM construction in the KS field has been previously published (Zhang et al 2014). Fig.…”

Section: In-situ Stress Field In Ksmentioning

confidence: 99%

A Study of the Interaction Mechanism between Hydraulic Fractures and Natural Fractures in the KS Tight Gas Reservoir

Zhang¹,

Qiu

Yang³

et al. 2015

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The KS reservoir is a naturally fractured, deep, tight gas sandstone reservoir under high tectonic stress. Development wells for this reservoir are of depths in excess of 6,500 m TVD. Stimulation is required to provide production rates that sufficiently compensate for the high cost of drilling and completing wells to access this deep reservoir. Hydraulic fracture design and execution must be optimal to ensure economic production. To effectively stimulate a more than 200-m thick sandstone reservoir yielding consistently high performance, it is critical to understand the interaction between hydraulic fractures and natural fractures, as the natural fractures significantly affect the growth and geometry of hydraulic fractures.To this end, a comprehensive study was conducted involving frac pressure analysis of previously stimulated wells, microseismic data analysis, hydraulic fracturing modeling by using a fracturing simulator that honors the natural fracture system, near-wellbore 4D geomechanical simulation of mechanical response of natural fractures during hydraulic fracturing, and large block hydraulic fracturing tests. This study reveals that existing natural fractures results in complexity of hydraulic fracture systems both in the near wellbore region and in the far field region. The complexity in the far field is largely controlled by the intersection angle (defined as the angle between the natural fracture strike and the maximum horizontal stress direction) given the large differential horizontal stress in this field.Based on an understanding of the interaction mechanism, an optimization of the hydraulic fracturing strategy was implemented in KS field. Improvements were made in staging, perforation, diversion, and the pumping schedule, which increased the averaged production rate more than 50% compared with previously stimulated wells.

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Section: In-situ Stress Field In Ksmentioning

confidence: 99%

A Study of the Interaction Mechanism between Hydraulic Fractures and Natural Fractures in the KS Tight Gas Reservoir

Zhang¹,

Qiu

Yang³

et al. 2015

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“…The rock Young's modulus and Poisson's ratio describe the rock deformation response under the deformation condition, which considers as the significant mechanical properties and can be used to predict the geomechanical behavior in the reservoir depletion (Villeneuve et al 2011;Zhang et al 2014). In order to understand the effects of Young's modulus and Poisson's ratio, the low, medium and high deformation of Young's modulus and Poisson's ratio are conducted in the numerical reservoir model (Table 2).…”

Section: Effect Of Young's Modulus and Poisson's Ratiomentioning

confidence: 99%

“…The ultra-deep natural gas resources are abundant in China, which are buried at more than 4500 m (Li et al 2021). There are several ultra-deep natural gas reservoirs discovered and put into development in China in recent years, such as Puguang, Kuqa, Yuanba and Anyue reservoirs (Zhang et al 2019). Ultra-deep natural gas reservoirs are playing significant roles in gas reserves and production increase in China, which is becoming increasingly more important in the strategic exploration position.…”

Section: Introductionmentioning

confidence: 99%

Multiphysics coupling study of near-wellbore and reservoir models in ultra-deep natural gas reservoirs

Cheng

Shen

et al. 2022

J Petrol Explor Prod Technol

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Understanding the changes of the near-wellbore pore pressure associated with the reservoir depletion is greatly significant for the development of ultra-deep natural gas reservoirs. However, there is still a great challenge for the fluid flow and geomechanics in the reservoir depletion. In this study, a fully coupled model was developed to simulate the near-wellbore and reservoir physics caused by pore pressure in ultra-deep natural gas reservoirs. The stress-dependent porosity and permeability models as well as geomechanics deformation induced by pore pressure were considered in this model, and the COMSOL Multiphysics was used to implement and solve the problem. The numerical model was validated by the reservoir depletion from Dabei gas field in China, and the effects of reservoir properties and production parameters on gas production, near-wellbore pore pressure and permeability evolution were discussed. The results show that the gas production rate increases nonlinearly with the increase in porosity, permeability and Young’s modulus. The lower reservoir porosity will result in the greater near-wellbore pore pressure and the larger rock deformation. The permeability changes have little effect on geomechanics deformation while it affects greatly the gas production rate in the reservoir depletion. With the increase in the gas production rate, the near-wellbore pore pressure and permeability decrease rapidly and tend to balance with time. The reservoir rocks with higher deformation capacity will cause the greater near-wellbore pore pressure.

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“…First, the input parameters (RHOB, ∆t comp and ∆t shear ) should be normalized using Equations (5)- (7). The normalized values (RHOB n , ∆t comp,n and ∆t shear,n ) are substituted in Equation 4to calculate the normalized value of the static Poisson's ratio (PR static,n ) with the optimized weights and biases listed in Table 3.…”

Section: Procedures To Use the Developed Empirical Equation To Predictmentioning

confidence: 99%

“…Geo-mechanical earth models are one of the tools used to represent the in-situ state of rock [5]. The development of such models depends on the in-situ stresses encountered within a formation, which can be estimated using the values of its elastic parameters, Poisson's ratio, and Young's modulus [6,7]. These parameters are very important in describing the elastic behavior of rock [8].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks

et al. 2019

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Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion design. The elastic behavior of rocks can be studied by determining two main parameters: Young’s modulus and Poisson’s ratio. Accurate determination of the Poisson’s ratio helps to estimate the in-situ horizontal stresses and in turn, avoid many critical problems which interrupt drilling operations, such as pipe sticking and wellbore instability issues. Accurate Poisson’s ratio values can be experimentally determined using retrieved core samples under simulated in-situ downhole conditions. However, this technique is time-consuming and economically ineffective, requiring the development of a more effective technique. This study has developed a new generalized model to estimate static Poisson’s ratio values of sandstone rocks using a supervised artificial neural network (ANN). The developed ANN model uses well log data such as bulk density and sonic log as the input parameters to target static Poisson’s ratio values as outputs. Subsequently, the developed ANN model was transformed into a more practical and easier to use white-box mode using an ANN-based empirical equation. Core data (692 data points) and their corresponding petrophysical data were used to train and test the ANN model. The self-adaptive differential evolution (SADE) algorithm was used to fine-tune the parameters of the ANN model to obtain the most accurate results in terms of the highest correlation coefficient (R) and the lowest mean absolute percentage error (MAPE). The results obtained from the optimized ANN model show an excellent agreement with the laboratory measured static Poisson’s ratio, confirming the high accuracy of the developed model. A comparison of the developed ANN-based empirical correlation with the previously developed approaches demonstrates the superiority of the developed correlation in predicting static Poisson’s ratio values with the highest R and the lowest MAPE. The developed correlation performs in a manner far superior to other approaches when validated against unseen field data. The developed ANN-based mathematical model can be used as a robust tool to estimate static Poisson’s ratio without the need to run the ANN model.

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Geomechanical Evaluation Enabled Successful Stimulation of a HPHT Tight Gas Reservoir in Western China

Cited by 8 publications

References 53 publications

A Study of the Interaction Mechanism between Hydraulic Fractures and Natural Fractures in the KS Tight Gas Reservoir

A Study of the Interaction Mechanism between Hydraulic Fractures and Natural Fractures in the KS Tight Gas Reservoir

Multiphysics coupling study of near-wellbore and reservoir models in ultra-deep natural gas reservoirs

A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks

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