Analysis of wellbore stability by pore pressure prediction using seismic velocity

Prankada, Milendra; Yadav, Kriti; Sircar, Anirbid

doi:10.1016/j.engeos.2021.06.005

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…It is one of the most important parameters of exploration and production wells. According to Eaton's method, the pore pressure (psi) is determined from the vertical stress and sonic log as follows [13,14]:…”

Section: Determination Of Vertical Stress and Pore Pressure -Vertical...mentioning

confidence: 99%

Integration of geological and well log and geomechanical modeling toward a safe mud weight for wellbore stability: A case study of Ruby field, Blocks 01&02, Cuu Long basin

Nguyen,

Pham

2023

PVJ

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There is a high density of drilled wells in Blocks 01&02 which is a part of the Cuu Long basin. The geological settings of this area have undergone a complicated evolution, resulting in heterogeneous lithology and variable stresses. Consequently, drilling activities in the Ruby oil field have faced numerous challenges, such as stuck pipe, gas kick, mud loss, and even lost well, etc. leading to substantial time and financial setbacks. It is essential to thoroughly understand the geomechanical characteristics of Ruby field to ensure safe operation and optimize drilling. In this paper, several downhole geophysical data sets such as wireline logging data as gamma ray (GR), density log (RHOB), neutron (NPHI), compression/shear sonic travel time (DTC/DTS), image formation log (FMI), pressure test (MDT) and leak of test (LOT, XLOT) are used to construct the 1D geomechanical model. The objective of this modelling is to compute parameters of pore pressure, vertical stress, horizontal stress, elastics properties, rock strengths. Then, these parameters are used to analyze the wellbore stability and to recommend appropriate drilling mud weights for the wells under study. This analysis can subsequently be extended to cover the entire Ruby oil field for the future drilling operations to enhance overall safety and efficiency.

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Section: Determination Of Vertical Stress and Pore Pressure -Vertical...mentioning

confidence: 99%

Integration of geological and well log and geomechanical modeling toward a safe mud weight for wellbore stability: A case study of Ruby field, Blocks 01&02, Cuu Long basin

Nguyen,

Pham

2023

PVJ

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“…In order to further illustrate the distribution of overpressure, a pressure profile needs to be created by examining the relationship between interval velocities and pressure coefficients. In the past few decades, many methods have used seismic velocities to predict overpressure (Pennebaker, 1968;Eaton, 1972;Kan and Swan, 2001;Dutta, 2002;Sayers et al, 2002;Carcione et al, 2003;Abiola and Ayenuro, 2021;Prankada et al, 2021). Figures 4, 5 show that there exists a clear response between the overpressured interval and P-sonic curve, and indicate that overpressure can be predicted by the deviation of the seismic velocity, so the method of Eaton (1972) was selected to identify the position of overpressure in profile C-D. To use Eaton's method, the deviation of the velocity from the normal compaction velocity needs to be estimated.…”

Section: Seismic Data Responses To Overpressure Distributionmentioning

confidence: 99%

Shallow Overpressure Formation in the Deep Water Area of the Qiongdongnan Basin, China

Ren

Xu²,

Shi³

et al. 2022

Front. Earth Sci.

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The scarcity of drilling in the deep water area of Qiongdongnan Basin restricts the cognition and prediction of overpressure. In this paper, a shallow zone of overpressure at the depth of 900–1,200 m below the sea floor in the deep water area was found by analyzing electronic logs, mud pressure (Mud pressure is a product of the height of the column of mud, density and gravity acceleration) and test pressure from drill stem testing (DST) and modular dynamic testing (MDT), and the interpretation of anomalous seismic interval velocities. The shallow overpressure is a newly observed geological phenomenon in the South China Sea for which the generation mechanisms are not well understood, despite similar observations and analyses elsewhere in the world. Two representative wells, one each located in the shallow water and the deep water areas, respectively were selected to investigate the vertical distribution of the shallow overpressure. The top of the overpressure in Well A in the shallow water area is about 2,111 m below sea floor, while the top of the overpressure in Well B in the deep water area is about 1,077 m below sea floor. A pressure coefficient (i.e., ratio of pore pressure to the normal hydrostatic pressure measured from the sea surface) profile was constructed from the shallow water area to the deep water area using the calibrated relationship between seismic interval velocities and pressure data from 30 wells. The distance between the top of the overpressure and the seabed is predicted to be between 900 and 1,200 m in the deep water area Basin. Disequilibrium compaction is the interpreted primary cause of the shallow overpressure and the results of basin modeling indicate that the shallow overpressure was generated since 5.5 Ma.

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“…BP is a very important parameter in the design of the fracture stimulation job. − BP also determines the economics and safety factor of the whole hydrofracking operations. − Rock formation BP, is closely linked to the in situ stresses and tensile strength of the rock. There has been a lot of effort put to predict the pore pressure of the formation using drilling and well log parameters, − but little attention has been given to predict the BP of the rock as a function of rock type and petrophysical and rock mechanical parameters of the rocks. To date, the oil industry is facing challenges to determine the representative BP with a reasonable accuracy.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Accelerated Approaches to Infer Breakdown Pressure of Several Unconventional Rock Types

et al. 2022

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Unconventional oil and gas reservoirs are usually classified by extremely low porosity and permeability values. The most economical way to produce hydrocarbons from such reservoirs is by creating artificially induced channels. To effectively design hydraulic fracturing jobs, accurate values of rock breakdown pressure are needed. Conducting hydraulic fracturing experiments in the laboratory is a very expensive and timeconsuming process. Therefore, in this study, different machine learning (ML) models were efficiently utilized to predict the breakdown pressure of tight rocks. In the first part of the study, to measure the breakdown pressures, a comprehensive hydraulic fracturing experimental study was conducted on various rock specimens. A total of 130 experiments were conducted on different rock types such as shales, sandstone, tight carbonates, and synthetic samples. Rock mechanical properties such as Young's modulus (E), Poisson's ratio (ν), unconfined compressive strength, and indirect tensile strength (σ t ) were measured before conducting hydraulic fracturing tests. ML models were used to correlate the breakdown pressure of the rock as a function of fracturing experimental conditions and rock properties. In the ML model, we considered experimental conditions, including the injection rate, overburden pressures, and fracturing fluid viscosity, and rock properties including Young's modulus (E), Poisson's ratio (ν), UCS, and indirect tensile strength (σ t ), porosity, permeability, and bulk density. ML models include artificial neural networks (ANNs), random forests, decision trees, and the K-nearest neighbor. During training of ML models, the model hyperparameters were optimized by the grid-search optimization approach. With the optimal setting of the ML models, the breakdown pressure of the unconventional formation was predicted with an accuracy of 95%. The accuracy of all ML techniques was quite similar; however, an explicit empirical correlation from the ANN technique is proposed. The empirical correlation is the function of all input features and can be used as a standalone package in any software. The proposed methodology to predict the breakdown pressure of unconventional rocks can minimize the laboratory experimental cost of measuring fracture parameters and can be used as a quick assessment tool to evaluate the development prospect of unconventional tight rocks.

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Analysis of wellbore stability by pore pressure prediction using seismic velocity

Cited by 5 publications

References 21 publications

Integration of geological and well log and geomechanical modeling toward a safe mud weight for wellbore stability: A case study of Ruby field, Blocks 01&02, Cuu Long basin

Integration of geological and well log and geomechanical modeling toward a safe mud weight for wellbore stability: A case study of Ruby field, Blocks 01&02, Cuu Long basin

Shallow Overpressure Formation in the Deep Water Area of the Qiongdongnan Basin, China

Machine Learning-Based Accelerated Approaches to Infer Breakdown Pressure of Several Unconventional Rock Types

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