Formation Pore Pressure Research Articles

Intelligent Pressure Monitoring Method of BP Neural Network Optimized by Genetic Algorithm: A Case Study of X Well Area in Yinggehai Basin

While drilling formation pressure monitoring is an important basis for ensuring drilling safety and oil and gas discovery, the calculation of existing pressure monitoring methods is complicated and the accuracy is difficult to improve. Taking the actual well data of well area X in Yinggehai Basin as the object, correlation analysis was first carried out to select and standardize the data features, and relevant effective parameters were extracted. Two kinds of neural networks, back-propagation network BP and back-propagation network GA-BP optimized by genetic algorithm, were used to establish artificial intelligence monitoring models of formation pressure based on 10 kinds of measuring and logging data, respectively. The application effect of the model was evaluated based on the results of monitoring the pressure while drilling. The results show that the monitoring accuracy of the BP neural network model is 91.25%, and that of the GA-BP neural network model is 92.89%. The latter has a better monitoring effect on formation pore pressure. In formation pressure monitoring in areas with a high degree of well control, the introduction of artificial intelligence technology has the advantages of simplicity, speed and high precision, and can provide a reference for other areas of pressure monitoring while drilling.

Formation pressure prediction method based on machine learning

Abstract Formation pore pressure is one of the key parameters in petroleum engineering. The high-temperature and high-pressure working conditions formed under the complex mechanism of Yingqiong Basin make downhole complex events such as overflow and well leakage occur frequently. To address the problem of the large prediction error of the traditional Eaton method under high temperature and high-pressure conditions, three machine learning methods, such as GA-BP(Genetic Algorithm-Back Propagation Neural Network), PSO-LSTM(Particle Swarm Optimization-Long Short-Term Memory Neural Network), SVR(Support Vector Regression), are introduced to find the nonlinear relationship between logging parameters and formation pore pressure. Seven logging parameters such as formation depth, mechanical drilling speed, drilling pressure, and rotational speed were used as input variables, and formation pore pressure was used as an output variable through correlation analysis for constructing the machine learning model. By analyzing the experimental results of the above three machine learning models with the Eaton method, the GA-BP model with lower error and more satisfying the needs of drilling engineering is preferred, and the various regression evaluation indexes of the model are also more excellent compared with other machine learning models, which are Mean Squared Error (MSE)=0.001, Mean Absolute Error (MAE)=0.003, and R-squared (R2)=0.991, respectively. It is concluded that the machine learning method is not only more advantageous in a class of nonlinear regression problems such as formation pore pressure prediction, but also can help engineers to predict formation pore pressure more accurately under the conditions of deep high-temperature, high-pressure, and complex formations, which contributes to guaranteeing the safety and high efficiency of drilling projects.

Borehole Stability of Natural Gas Hydrate Reservoirs

Abstract The decomposition of natural gas hydrate (NGH) due to long-term drilling may decrease the strength of reservoirs and increase the borehole failure risk. Meanwhile, the creep properties of NGH reservoirs also may give rise to engineering problems including borehole shrinkage and jamming of drilling tools in the late stage of drilling, which seriously impairs the drilling safety. A thermal-fluid-solid multi-field coupling model considering the decomposition kinetics of NGH and creep properties of reservoirs was established to explore the influence mechanism of NGH decomposition and creep properties of hydrate-bearing sediments on mechanical behaviors of boreholes. In addition, influences of drilling parameters and geomechanical properties of reservoirs on borehole stability in the drilling process of NGH reservoirs were analyzed. Research results show that creep properties of NGH deposits causes homogenization of stress distribution in different directions of NGH reservoirs around boreholes, transition of plastic zones around boreholes to a round shape, and possibly overall borehole collapse. As the horizontal geostress becomes more inhomogeneous, the borehole instability risk increases; the higher the initial pore pressure of formation is, the lower the effective geostress and the lower the borehole instability risk. The research results can provide theoretical support for safe drilling of NGH reservoirs in South China Sea and promote the early commercial exploitation of NGH resources in China.

Optimizing Refracturing Models in Tight Oil Reservoirs: An Integrated Workflow of Geology and Engineering Based on Seepage-Stress Real-Time Cross-Coupling Strategy

Summary Integrated modeling of refracturing is a comprehensive and complex task for engineers involved in field development, especially in previously developed mature fields. The modeling process requires consideration of numerous factors to accurately predict fracture propagation patterns during refracturing and production. During the initial fracturing process, hydraulic fractures generate an altered induced stress field. Later, during the post-fracturing production process, hydrocarbon extraction causes formation pore pressure depletion, leading to further alterations in the stress field. However, due to the complexity of these phenomena, most existing workflows simplify the coupled simulation problems of stresses at different stages in the modeling process. Consequently, this often results in questionable hydraulic fracture geometries during refracturing and suboptimal refracturing designs. The goal of this study is to develop a novel integrated workflow for refracturing, specifically tailored for complex fracture networks in tight oil reservoirs. This model incorporates the hydraulic fracture propagation process during the initial fracturing and dynamic stress changes during the initial production process. It employs artificial intelligence algorithms to calibrate the wellhead treating pressure using a physics-based model, enabling a better understanding of the initial fracturing fracture geometries. The production history match is then conducted based on the initially calibrated hydraulic fracture geometries, preserving the precision of the original fracture geometry. In addition, geomechanics modeling is conducted to obtain dynamic stress changes during the initial production process. For the refracturing design, the fracture propagation model for the refracturing process is later conducted on the depleted stress field. Following a 240-day period after refracturing, the production history is matched using an artificial intelligence–assisted reservoir simulator. Our results indicate that, due to prolonged production, significant changes occur in the stress field during the initial development period, with an average horizontal stress deviation angle of approximately 35° in the near-well zone. With the combined influence of the changing stress field and natural fractures, refracturing results in longer and more complex hydraulic fracture geometries, ultimately increasing individual well production.

Pore pressure prediction based on rock physics theory and its application in seismic inversion

Formation overpressure seriously affects drilling and downhole operation. Accurate prediction on the formation pore pressure can not only reduce the probability of drilling accidents, but also quantitatively evaluate the original formation pressure of underground pore space, which provides an important reference for site selection of carbon sink projects using underground space resources such as CO2 geological storage. It is therefore necessary to set up a widely applicable method that is based on rock physics theory and conforms to the characteristics of rock mechanics and fluid mechanic. This method is suitable for both logging prediction and seismic inversion of pore pressure. The traditional method of predicting pore pressure based on P-wave velocity has multiple solutions, and the prediction based on S-wave velocity which is not sensitive to fluid has new significance. Based on the Hertz-Mindlin petrophysical model that considering pressure variation and the Gassmann fluid substitution equation that addresses the change in fluid saturation, this paper firstly derived rock physical formulas for predicting pore pressure in logging, and then derived the intrinsic power function relationship between the effective pressure (Pe) and S-wave velocity (Vs) as well as S-wave impedance (Is). Based on this, a set of geophysical methods integrating S-wave velocity prediction, pore pressure prediction in well and seismic inversion is finally established. The efficacy of this method has been well validated, with an average error of 2.35% in S-wave velocities prediction, 4.5% in single-well pore pressure prediction. The results of seismic inversion of pore pressure are consistent with the phenomenon of overpressure development in actual working area. This method can be further extended to other areas, providing invaluable reference for underground operation such as oil and gas exploration and CO2 geological storage.

Smart predictions of petrophysical formation pore pressure via robust data-driven intelligent models

AbstractPredicting pore pressure in the formation is crucial for assessing reservoir geomechanical characteristics, designing drilling schemes/mud programs, and strategies to enhance oil recovery. Accurate predictions are vital for safe and cost-effective exploration and development. Recent research has seen the emergence of intelligent models utilizing machine learning (ML) and deep learning (DL) algorithms, offering promising outcomes. However, there remains a need to identify the most accurate and dependable model among these. This study aims to address this gap by comparing the performance of various ML and DL models, as reported in existing literature, to determine the optimal approach for pore pressure prediction. The sorted machine learning (ML) and deep learning (DL) regression algorithms used for the comparative analysis are decision tree (DT), extreme gradient boosting (XGBoost), random forest (RF), recurrent neural network (RNN), and convolutional neural network (CNN). A total dataset of 22,539 is gathered from five wells (15/9-F-1 A, 15/9-F-1 B, 15/9-F-11 A, 15/9-F-11 T2, and 15/9-F-14) drilled at North-sea Volve oil field, Norway. The first four wells are used to train and test the ML and DL algorithm, and the remaining well (15/9-F-14) is used to evaluate the best-performing algorithm’s universality in predicting pore pressure at the field of study. Seven different petrophysical parameters are used as input parameters to develop the predictive models. Statistical performance metrics are carried out to analyze the applied ML and DL performance. Based on performance indicators, the RF algorithm showed superior results compared to other predictive models with R2 and RMSE values of 0.97 and 2.70 MPa, respectively. Furthermore, the best-performing predictive model with low prediction error RMSE value is applied to the other well dataset from the field of study to access the universality of the RF algorithm to predict pore pressure in the field of study. The results of the universality analysis show a satisfactory prediction accuracy with R2 and RMSE values of 0.905 and 6.48 MPa, respectively.

Extended Aquifer System Pressure Behavior under Carbon Storage

Summary Most reported carbon storage projects have involved inexpensive carbon dioxide (CO2) capture from gas processing plants or ethanol refineries. However, widespread carbon capture and storage (CCS) application must avoid any risk that high capital investment cost for carbon capture from stationary point sources leads to unanticipated issues related to the aquifer storage. This paper reviews successful and unsuccessful carbon storage projects and explains simple extended aquifer system fundamentals that must be considered in selecting a storage aquifer. This study begins by evaluating reported carbon storage projects in the context of an extended aquifer system with specific attention to initial formation pore pressure and potential or known hydraulic vertical or lateral communication with hydrocarbon accumulations and/or fresh water. Further study focuses on how the contrast between injection well and aquifer pressure evolution enables understanding of the overall aquifer material balance. Finally, we consider implications of brine migration during and after long-term CO2 injection in unconfined aquifers. Experience in the petroleum industry with aquifer behavior includes presence or lack of water influx and production from hydrocarbon reservoirs that share a common aquifer. Of particular importance is the observation that hydrostatic initial formation pressure indicates the possibility that a petroleum system, or an extended aquifer system without hydrocarbon accumulation(s), connects to atmospheric pressure through an unconfined aquifer. In such cases, indefinite injection will never increase the regional aquifer pressure. Furthermore, initial formation pressure that exceeds hydrostatic pressure implies a petroleum system or an extended aquifer system that is volumetrically limited. In such cases, injection will increase the system pressure, and pressure monitoring can detect leakage from the system. Finally, CO2 injection into an aquifer will displace brine in the direction of lower pressure that could relate to distant production from the same aquifer or from hydrocarbon reservoirs with which it communicates. Reasons for known carbon storage project interruptions have included unexpected lateral plume migration or aquifer pressure increase during CO2 injection that might have been anticipated with attention to straightforward consideration of aquifer-enabled hydraulic communication. Such extended aquifer dynamics must be included in long-term models for permanent CO2 storage during and after injection.

An Improved Continuum Approach for Unconsolidated Formations on the Field Scale

Summary With the development of unconventional resources, such as oil sands and methane hydrate reservoirs, the importance of the mechanical performance model for underground unconsolidated rocks has increased significantly. The commonly used numerical approach for unconsolidated rocks is the discrete-element method (DEM). However, the extensive calculations required by the DEM make it inadequate for simulating unconsolidated rock behavior on a field scale. An alternative is the continuum approach, used to simulate the behavior of unconsolidated rocks on the field scale. In previous continuum approaches, unconsolidated rocks have been modeled as a visco-plastic fluid (i.e., Bingham fluid). The continuum approach based on visco-plastic fluid uses pressure (scalar) to describe the stress state of the particles. However, this approach does not account for the difference between the maximum and minimum principal stresses of the in-situ stress field when simulating the mechanical performance of unconsolidated rocks. Here, we developed an improved continuum approach for unconsolidated rocks and used the finite-element method as a numerical approach. Our improved model can consider the difference between the maximum and minimum principal stresses of the in-situ stress field and the pore pressure of the unconsolidated formation. We validated our numerical model with the angle of repose test, a benchmark problem for unconsolidated rocks. The validation results confirm the accuracy of our unconsolidated model. For the coupled model between the unconsolidated model and the flow model, we used an analytical solution to verify its reliability. Unconsolidated rock performances in an unconsolidated reservoir with fluid injection have been investigated based on our coupled model. The simulation results show that injection can activate the movement of unconsolidated rock particles, leading to changes in the distribution of effective stress and permeability. Our model has the potential to address large-scale unconsolidated rock issues and contribute to energy extraction.

Predicating of Formation Pore Pressures in Tertiary Reservoirs Using Geophysical Wireline Log Data

Changes to certain geophysical characteristics, such as sonic transit time with depth, indicate the pressure system of over-pressured zones. In this study, well-log data is used to investigate the distribution of formation overpressures in the tertiary reservoirs of one Iraqi oil field. Three wells located within the tertiary carbonate reservoir of one Iraqi oil field are of interest in this study. To assess the reservoirs, a petrophysics log analysis was carried out. The reservoir's intervals are the area of interest. The sonic velocity data was used to create a normal compaction trend line, and the overpressure zones were identified by observing the reversals in the normal compaction trend line. The pore pressure, fracture pressure, and gradients were calculated using the Eaton method. The method relies on the correlation between the normal transit time found on the normal compaction trend and the observed transit time from the log reading. The sonic log determines the bulk density and matches the density derived from the density log. Abnormal pressures were identified primarily within the area of interest, especially with unit B in Well 1. In Well 2, the abnormal pressures were recognized in zones above the area of interest. Abnormal pressures in Well 3 were identified primarily within all areas of interest. The importance of this work is to help predict overpressure zones in the research area before drilling or workover.

Analysis of Surge and Swab ECD of Herschel Bulkley Fluids in Rumaila Iraqi Oil Field

Surge and swab pressures are frequently produced during various stages of well construction, including casing running and tripping operations. Managing downhole pressure within the mud window is crucial for mitigating the risks associated with drilling operations including wellbore failure, lost circulation, kicks, and well control issues. The primary objectives of this study are to emphasize the theoretical foundation of surge and swab pressures, forecast the optimum pipe and casing tripping speeds, as well as identify the changes in surge and swab pressures (i.e., equivalent circulating density-ECD) for both open-ended and close-ended drill strings. To achieve these goals, a steady state surge and swab model was used to simulate a case study in the Rumaila oil field, located in Southern Iraq, by utilizing landmark-well plan software. The results of this study support the evidence that the string trip speed plays a substantial role in controlling the swab and surge pressures. It was found that pulling out the 5" drill string from the 12 1/4" open hole section drilled with a 9.51 ppg water-based mud at a speed of 10 sec/stand resulted in a swab ECD below the formation pore pressure against all formations, thus resulting in a kick. Moreover, it was found that the annular clearance had a significant impact on the surge and swab ECD whereas the surge ECD at the bit was bigger than that obtained at the previous hole casing shoe. For example, in our case study pulling speeds ranging from 10-190 sec/stand for the 9 5/8" casing could cause the swab ECD to be below the formation pore pressure gradient at total depth (TD), which is 9.23 ppg. While it was safe to run the same casing through the same interval within 55-190 sec/stand. Furthermore, the results emphasized high trip speeds ranging between 10-30 sec/stand, where for close-ended drill strings, greater surge ECD was observed compared to the open-ended ones. The surge ECD of a 5" close-ended drill string at 30 sec/stand (60 m/min) at bit was 9.92 ppg, while an open-ended string at the same speed was 9.9 ppg.

Building 1D Mechanical Earth Model for Subba Oilfield in Iraq

At the Subba oil field, wellbore stability is the main concern while drilling. The wellbore's instability causes several issues, including: (inefficient hole cleaning, tight hole, stuck pipe, mud losses, caving, bad cementing, and well kick or blowout). This increases Non-Productive Time (NPT) and well-drilling costs; hence the operator's main goal is to create a drilling program that reduces these problems and therefore reduce drilling cost. The study aims to build a 1D mechanical earth model to predict the wellbore failure and design optimum mud weight to improve the drilling efficiency for future wells. The model includes pore pressure, stress state, and rock mechanical parameters (such as UCS, angle of friction, Young-Modulus, and Poisson-Ratio). To achieve this aim, the study utilised offset well data including log data (Gama-Ray Logs (GR), Caliper Logs (CALI), Density Logs (RHOZ), and Compressional Sonic (DTCO) and Shear Sonic (DTSM)), Core tests, Mini-frac field tests, Drilling Reports, Mud Reports, and Mud Log Reports (master log) to estimate and calibrate the profiles of formation pore pressure, rock mechanical properties, and in-situ stresses. The 1D mechanical earth model was built using the Excel program for three wells data set, where all the necessary parameters to create the model was calculated, calibrated for the calculated variables with core data and pressure test points, and finally the safe mud window was detected. The results showed that the Eaton Slowness method to predict pore pressure perfectly matches the pressure test points. The most common fault regimes in the Subba oilfield are normal and strike-slip faults. The Modified Lade criteria showed a compatible match with drilling events and calliper log in predicting the failure zones, so it is the best criterion in determining minimum and maximum mud weight. Based on the results of this study and in comparison with the mud window used in drilling operations in the field, it is necessary to change the mud window used in drilling and adopt the safe MWW of this study in drilling new wells in this field and the area adjacent to the field.

Prediction method for formation pore pressure based on transfer learning

Pore pressure prediction of drilled wells is the recognition of formation information using logging and other actual drilling data after drilling is completed, which is of great significance in improving the mastery of regional formation information and reducing the potential risks of the engineering program of wells to be drilled. Traditional formation pressure prediction methods rely on manual experience and are difficult to cope with complex formations. Existing machine learning-based methods have high accuracy in the prediction of formation pressure for wells with similar data composition in the same block or logging, but have poor accuracy in the prediction of wells with large differences in data from different blocks or logging. In this paper, a transfer learning algorithm is introduced to modify the existing LSTM-based formation pore pressure prediction model and establish a transfer learning-based formation pore pressure prediction method. In addition, we evaluated the prediction performance of machine learning algorithms and transfer learning algorithms under the same/different blocks and the same/different logging data conditions. The results show that, compared with the existing machine learning algorithms, for the same block and the same logging data conditions, transfer learning can improve the prediction accuracy by 0.15%; for the same block and different logging data conditions, transfer learning can improve the prediction accuracy by 0.33%; for the different block and the same logging data conditions, transfer learning can improve the prediction accuracy by 0.19%; for the different block and different logging data conditions, transfer learning can improve the prediction accuracy by 0.11%. Overall, the addition of the transfer learning method can improve the accuracy and generalization ability of the formation pore pressure prediction model based on machine learning to a larger extent.

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering. Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction. However, most of the traditional deep learning models are less efficient to address generalization problems. To fill this technical gap, in this work, we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data. Specifically, the new model, named CGP-NN, consists of a novel parametric features extraction approach (1DCPP), a stacked multilayer gated recurrent model (multilayer GRU), and an adaptive physics-informed loss function. Through machine training, the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction. The CGP-NN model has the best generalization when the physics-related metric λ=0.5. A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels. To validate the developed model and methodology, a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability. The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

Experimental study of a multimechanism formation pressure prediction model influenced by shale content

The precise prediction of abnormally high-pressure formations has always been a hot topic in oil and gas exploration and development. In the Yingqiong Basin of the South China Sea, abnormally high-pressure formations are widespread, with narrow drilling safety windows and frequent accidents caused by the limited accuracy of formation pressure prediction. To improve the prediction accuracy of formation pressure, based on the characterization and classification method of the pressure forming mechanism of sediment and mechanics and considering the influence of shale content on pore pressure, in this study, soil consolidation acoustic experiments were carried out using real rock debris soil in a block of strata in this area. We obtained the response laws of parameters such as acoustic velocity and density under different pressure-forming mechanisms and established an acoustic density intersection chart exhibiting the influence of shale content. The experimental results show that under the same loading mechanism and conditions, the higher the shale content in the core is, the smaller the density, the smaller the acoustic velocity, and the smoother the trend line of the acoustic density intersection chart. When the shale content ≥0.5, under the unloading mechanism, the higher the shale content of the rock is, the further away the unloading trend line is from the loading trend line, and the greater the difference in the changes in the acoustic properties of the rock compared to the density properties. Moreover, the experimental conclusion also verified why the pressure formation mechanism in non-shale formations is difficult to determine. Based on the experimental results, the traditional Bowers model was improved, and a multimechanism formation pressure prediction model for the influence of shale content was established. The established model was validated and compared. The evaluation results showed that compared to the traditional model, the maximum prediction error of the multimechanism prediction model was reduced by approximately 20%, and the average prediction error was approximately 3%. This model effectively improved the prediction accuracy of the formation pore pressure.

Using One-Dimensional Geomechanical Model for Determining the Safe Wellbore Orientation of Zubair Reservoir, Southern Iraq

The wellbore instability issues are firmly occurring in the Iraqi oilfield and particularly in the southern region of Iraq. It is causing a significant loss of time dealing with many types of downhole problems, as well as an increase in the operations costs because of the instability consequences. By using the well information and open-hole logging data, the One-Dimensional (1-D) geomechanical model generated with Schlumberger Techlog software (V.2015) was used to assess the mechanical stability of the wellbore. The model was calibrated and validated by using core data (Triaxial test), the formation pore pressures, and the Mini-frac test). The 1-D model was validated across the actual well conditions (i.e., the original mud weight and the actual wellbore failure) by using three criteria of failures which are Modified Lade, Mogi-Coulomb, and Mohr-Coulomb. The validation process showed that the failure criterion of Mogi-Coulomb is better than the other two criteria in presenting the actual borehole failure, and thus it can be used to develop the plans of wellbore orientation. By investigating wellbore orientation with various scenarios, ranging from a vertical trajectory to the horizontal strategy, the wellbore orientation is mainly safe (with the minimum breakout) in vertical and inclined wells up to 40º with minimum mud weight of 11.7 and 12.6 ppg. The optimum wellbore trajectory should be in the direction of minimum horizontal stress, which is North-West to South-East (130º-160º). The developed plans provide a guideline for performing stable drilling operations in the south of Iraq and the nearby areas, thus effectively contributing in reduction the overall cost.

Double-porosity poromechanical models for wellbore stability of inclined borehole drilled through the naturally fractured porous rocks

Fractured porous rock comprises the matrix and fracture systems with double-porosity and double-permeability. This paper treats the fracture system as a porous media due to the fracture-occupied space filled by the depositional minerals or assumes it to be fully saturated with the formation fluid. Thus, one considers the double-porosity poromechanical models and provides the semi-analytical dual-poroelastic solutions to the field variables around arbitrally inclined boreholes subjected to non-hydrostatic stresses. The plane strain assumption holds for deriving solutions to field variables. The field variables include displacements, stresses, and two pore-pressures in the matrix system and fracture system. This work conducts a quantitative study of the relation between fracture spacing related to fracture density, fracture width, and the safe pressure ratios window (SPRW). One defines that the upper limit of SPRW is related to the ratio of fracture pressure to the original formation pore pressure, and the lower limit is referred to as the ratio of collapse pressure to formation pore pressure. The main results show that the dual-porosity medium displays a higher wellbore instability potential than the single-porosity one. The fracture system that is fully saturated with formation fluid has a narrower SPRW than that of the minerals-filled one referred to as a porous medium. When the coupled hydro-mechanical model is considered, the drilling mud-pressure window narrows with increasing time. It suggests that the drilling engineer considers fracture spacing, and fracture width in the predrilling design of the time-dependent SPRW of naturally fractured porous rocks.

Study on Fault Activation Evaluation Mechanism and Sensitivity Analysis in Shale Gas Hydraulic Fracturing

Shale gas hydraulic fracturing usually activates nearby faults and makes them slip. In horizontal wells, fault slip can result in serious casing deformation. Casing deformation slows the fracturing process, lowers production, and raises the cost of a shale gas well. It is challenging to obtain underground data on fault activation because of the deep shale reservoirs. As a result, the current study needed to indicate how hydraulic fracturing affects fault activation length. This made it challenging to control casing deformation. The fluid-structure coupled finite element method was used in this study to create a coupled seepage-stress model for heterogeneous shale formation. With microseismic signs and hydraulic fracking for shale gas, this model examined the variation law of pore pressure and ground stress. The fault activation coefficient was created to assess the fault activation duration and the impact of hydraulic fracturing. The model was verified by the microseismic signal. The outcomes of the numerical simulation demonstrate how the rapid rise in formation pore pressure during hydraulic fracturing affected the ground stress at the fault interface. The influence of ground stress variation at the fault interface on fault activation could not be ignored. Increased fault elastic modulus, fracture pressure, fracture time, and the fault Poisson ratio result in longer fault activation lengths. The length of the fault’s activation was decreased by the increase in fracture stage, distance from the fault, friction angle within the fault, and fault angle. Finally, a shale gas horizontal well with casing deformation in block C was analyzed. The results showed that reducing the fracturing duration can reduce the activation length of the fault by 68.25%, resulting in a 9.1 mm fault slide and a 0.86 mm casing deformation, respectively. This study offers theoretical guidelines for preventing fault activation during hydraulic fracturing in horizontal shale gas wells.

Uniquely determine fracture dimension and formation permeability from diagnostic fracture injection test

Estimating formation permeability is crucial for production estimation, hydraulic fracturing design optimization and rate transient analysis. Laboratory experiments can be used to measure the permeability of rock samples, but the results may not be representative at a field scale because of reservoir heterogeneity and pre-existing natural fracture systems. Diagnostic Fracture Injection Tests (DFIT) have now become standard practice to estimate formation pore pressure and formation permeability. However, in low permeability reservoirs, after-closure radial flow is often absent, which can cast significant uncertainties in interpreting DFIT data. Without knowing the fracture dimension prior, open fracture stiffness/compliance can't be determined, which is required for formation permeability estimation. Previous work has to assume a fracture radius or fracture height in order to estimate formation permeability, thus dent the confidence in the interpretation results. In the study, we present a new approach to determine fracture dimension, leak-off coefficient and formation permeability uniquely based on material balance and basic fracture mechanics, using data from shut-in to after-closure linear flow. Field examples are also presented to demonstrate the simplicity and effectiveness of this new approach.

Discrete Measurements of the Least Horizontal Principal Stress from Core Data: An Application of Viscoelastic Stress Relaxation

Summary The emerging Paradox Oil Play in southeastern Utah is among the most significant unconventional plays in the western USA. The mean total undiscovered oil resources within just the Pennsylvanian Cane Creek interval of the Paradox Basin are believed to exceed 215 million barrels. However, to date, less than 5% (~9 million barrels) of the total Cane Creek resource has been produced from fewer than 40 wells, and only approximately one-half of those are horizontal wells. More than 95% of production is from the central Cane Creek Unit (CCU). Natural fractures are a key feature of many production wells, but stimulation by induced hydraulic fractures is not consistently successful. We hypothesize that more effective production in this play will rely on better fundamental characterization, especially on better quantification of the state of stress. Approximately 110 ft of core, well logs, and a diagnostic fracture injection test (DFIT) were acquired from the State 16-2 well within the CCU. With these data, we applied two methods to constrain and clarify the state of stress. The first technique, the Simpson’s coefficient method, provides lower bounds on the two horizontal principal stresses and relies on only limited data. Alternatively, the viscoelastic stress relaxation (VSR) method is used to estimate the least horizontal principal stress, building on observations that principal stresses become more isotropic as the viscous behavior of a rock is more pronounced. Results of these two methods support the hypothesis that the state of stress in the CCU of the Paradox Basin is nearly lithostatic and isotropic. Other factors consistent with this hypothesis include high formation pore pressure, which tends to reduce the possible stress states by changing the frictional failure equilibrium; lack of induced fractures in the core, which should be present in the case of stress anisotropy; and interbedded halite layers, which given their high degree of ductility, probably lead to greater VSR for the entire sedimentary package.

Overpressure characteristics and geological origin of the paleogene in Baiyun Sag, Pearl River Mouth Basin

The accurate understanding of the origin of overpressure is the basis of formation pressure research and the core work of determining the distribution of formation pore pressure in drilling design. The deep water area of Baiyun Sag in the Pearl River Mouth Basin in the northern margin of the South China Sea has been proved to be a hydrocarbon rich depression. In recent years, overpressure has been encountered in the middle and deep layers, but the geological origin of the overpressure in the deep water area are still unclear. Therefore, the pore pressure profile of the Baiyun Sag was evaluated using the data of drilling and seismic velocity, and the relationship between overpressure and the generation mechanism was established. Most of the surrounding and shallow strata of Baiyun Sag show normal pressure, while the middle and deep strata generally show overpressure, with the maximum pressure coefficient up to 1.55. The overpressure zone mainly develops from the middle of Zhujiang Formation to the upper member of Enping Formation. The mudstone resistivity and density values of the two typical Wells deviated from the normal trend. The density showed a constant trend with the increase of depth, while the resistivity showed a certain degree of reversal. The upper layer of the overpressure zone showed abnormally high resistivity values, corresponding to the overgland and transition zone formed by high carbonate content. There is no obvious increase of porosity in the overpressured sandstone strata of Baiyun sag, which does not have the characteristics of undercompaction. The velocity in the overpressure section of the two Wells varies greatly, and the density is basically unchanged or slightly decreased. It is concluded that the overpressure is caused by fluid expansion from hydrocarbon-generation. The large-scale and long-term fluid expansion caused by hydrocarbon generation and expulsion of source rocks provides the power source for the formation of overpressure in Baiyun sag. The distribution of effective source rocks has a controlling effect on the distribution range of overpressure. The tight sandstone strata in the Zhuhai, Wenchang and Enping formations become the cover layer of the overpressure system in the longitudinal direction.