Impact of geomechanical heterogeneity on multiple hydraulic fracture propagation

To extract more gas from shale gas reservoirs, the spacing among hydraulic fractures should be made smaller, resulting in a significant stress shadow effect. Most studies regarding the stress shadow effect are based on the assumption of homogeneity in rock properties. However, strong heterogeneity has been observed in shale reservoirs, and the results obtained with homogeneous models can be different from practical situations. A series of case studies have been conducted in this work to understand the effects of mechanical heterogeneity on multiple fracture propagation. Fracture propagation was simulated using the extended finite element method. A sequential Gaussian simulation was performed to generate a heterogeneous distribution of geomechanical properties. According to the simulation results, the difficulty of fracture propagation is negatively correlated with the Young's modulus and Poisson's ratio, and positively correlated with tensile strength. When each of the multiple fractures propagates in a homogeneous area with different mechanical properties, the final geometry of the fracture is similar to homogeneous conditions. When the rock parameter is a random field or heterogeneity perpendicular to the propagation direction of fracture, the fracture will no longer take the wellbore as the center of symmetry. Based on the analysis of fracture propagation in random fields, a small variance of elastic parameters can result in asymmetrical propagation of multiple fractures. Moreover, the asymmetrical propagation of hydraulic fractures is more sensitive to the heterogeneity of Poisson's ratio than Young's modulus. This study emphasises the importance of considering geomechanical heterogeneity and provides some meaningful suggestions regarding hydraulic fracturing designs.

Biot's equations-based reservoir parameter inversion using deep neural networks

Reservoir parameter inversion from seismic data is an important issue in rock physics. The traditional optimisation-based inversion method requires high computational expense, and the process exhibits subjectivity due to the nonuniqueness of generated solutions. This study proposes a deep neural network (DNN)-based approach as a new means to analyse the sensitivity of seismic attributes to basic rock-physics parameters and then realise fast parameter inversion. First, synthetic data of inputs (reservoir properties) and outputs (seismic attributes) are generated using Biot's equations. Then, a forward DNN model is trained to carry out a sensitivity analysis. One can in turn investigate the influence of each rock-physics parameter on the seismic attributes calculated by Biot's equations, and the method can also be used to estimate and evaluate the accuracy of parameter inversion. Finally, DNNs are applied to parameter inversion. Different scenarios are designed to study the inversion accuracy of porosity, bulk and shear moduli of a rock matrix considering that the input quantities are different. It is found that the inversion of porosity is relatively easy and accurate, while more information is needed to make the inversion more accurate for bulk and shear moduli. From the presented results, the new approach makes it possible to realise accurate and pointwise inverse modelling with high efficiency for actual data interpretation and analysis.

Application of seismic multiattribute machine learning to determine coal strata thickness

The coal mining industry is developing automated and intelligent coal mining processes. Accurate determination of the geological conditions of working faces is an important prerequisite for automated mining. The use of machine learning to extract comprehensive attributes from seismic data and the application of that data to determine the coal strata thickness has become an important area of research in recent years. Conventional coal strata thickness interpretation methods do not meet the application requirements of mines. Determining the coal strata thickness with machine learning solves this problem to a large extent, especially for issues of exploration accuracy. In this study, we use seismic exploration data from the Xingdong coal mine, with the 1225 working face as the research object, and we apply seismic multiattribute machine learning to determine the coal strata thickness. First, through optimal selection, we perform seismic multiattribute extraction and optimal multiparameter selection by selecting the seismic attributes with good responses to the coal strata thickness and extracting training samples. Second, we optimise the model through a trial-and-error method and use machine learning for training. Finally, we illustrate the advantages of this method using actual data. We compare the results of the proposed model with results based on a single attribute, The results show that application of seismic multiattribute machine learning to determine coal strata thickness meets the requirements of geological inspection and has a good application performance and practical significance in complex areas.

Uncertainty analysis of numerical inversions of temperature logs from boreholes under injection conditions

Conventional methods to estimate the static formation temperature (SFT) require borehole temperature data measured during thermal recovery periods. This can be both economically and technically prohibitive under real operational conditions, especially for high-temperature boreholes. This study investigates the use of temperature logs obtained under injection conditions to determine SFT through inverse modelling. An adaptive sampling approach based on machine-learning techniques is applied to explore the model space efficiently by iteratively proposing samples based on the results of previous runs. Synthetic case studies are conducted with rigorous evaluation of factors affecting the quality of SFT estimates for deep hot wells. The results show that using temperature data measured at higher flow rates or after longer injection times could lead to less-reliable results. Furthermore, the estimation error exhibits an almost linear dependency on the standard error of the measured borehole temperatures. In addition, potential flow loss zones in the borehole would lead to increased uncertainties in the SFT estimates. Consequently, any prior knowledge about the amount of flow loss could improve the estimation accuracy considerably. For formations with thermal gradients varying with depth, prior information on the depth of the gradient change is necessary to avoid spurious results. The inversion scheme presented is demonstrated as an efficient tool for quantifying uncertainty in the interpretation of borehole data. Although only temperature data are considered in this work, other types of data such as flow and transport measurements can also be included in this method for geophysical and rock physics studies.

Brittle mineral prediction based on rock-physics modelling for tight oil reservoir rocks

The reservoir rocks from Chang-7 member of Yanchang Formation of Ordos Basin are characterised with heterogeneous fabric structures at the pore scale, and low porosity/permeability is exhibited at the macro scale. Precise prediction of reservoir brittleness is of great significance to oil production. Ultrasonic experiments are performed on tight sandstones collected from the target formation. A rock-physics model (RPM) is presented based on the Voigt–Reuss–Hill average (VRH), self-consistent approximation (SCA) and differential effective medium (DEM) theory. The brittleness characteristics relying on mineral composition, porosity and microcrack properties are explored by using the RPM. The Young's modulus increases and Poisson ratio decreases with increasing quartz content. Based on experimental, log and seismic data, brittle mineral analysis of rock physical model is performed at multiple scales. The model accuracy is verified by experimental data and well log data. The brittleness distribution is predicted on the basis of log and seismic data, which can be instructive for the reservoir rock fracturing in actual engineering operations.

Application of an improved particle filter for random seismic noise suppression

Random noise is inevitable during seismic prospecting. Seismic signals, which are variable in time and space, are damaged by conventional random noise suppression methods, and this limits the accuracy in seismic data imaging. In this paper, an improved particle filtering strategy based on the firefly algorithm is proposed to suppress seismic noise. To address particle degradation problems during the particle filter resampling process, this method introduces a firefly algorithm that moves the particles distributed at the tail of the probability to the high-likelihood area, thereby improving the particle quality and performance of the algorithm. Finally, this method allows the particles to carry adequate seismic information, thereby enhancing the accuracy of the estimation. Synthetic and field experiments indicate that this method can effectively suppress random seismic noise.

A high-resolution dispersion imaging method of seismic surface waves based on chirplet transform

The surface-wave analysis method is widely adopted to build a near-surface shear-wave velocity structure. Reliable dispersion imaging results form the basis for subsequent picking and inversion of dispersion curves. In this paper, we present a high-resolution dispersion imaging method (CSFK) of seismic surface waves based on chirplet transform (CT). CT introduces the concept of chirp rate, which could focus surface-wave dispersion energy well in time-frequency domain. First, each seismic trace in time-distance domain is transformed to time-frequency domain by CT. Thus, for each common frequency gather, we obtain a series of 2D complex-valued functions of time and distance, which are called pseudo-seismograms. Then, we scan a series of group velocities to obtain the slanting-phase function and perform a spatial Fourier transform on the slanting-phase function to get its amplitude. In addition, power operation is adopted to increase the amplitude difference between dispersion energy and noise. Finally, we generate the dispersion image by searching for the maximum amplitude of a slanting-phase function. Because the CSFK method considers the position of surface-wave energy in the time-frequency domain, this largely eliminates the noise interference from other time locations and improves the resolution and signal-to-noise ratio of the dispersion image. The results of synthetic test and field dataset processing demonstrate the effectiveness of the proposed method. In addition, we invert all 120 sets of dispersion curves extracted from reflected wave seismic data acquired for petroleum prospecting. The one-dimensional inversion shear-wave velocity models are interpolated into a two-dimensional profile of shear-wave velocity, which is in good agreement with the borehole data.

A calculation methodology of fault relative displacement used to study the mechanical characteristic of fault slip

Fault slip caused by mining disturbance is a crucial issue that can pose considerable threats to the mine safety. This paper proposes a point-by-point integration calculated methodology of fault relative slip and studies fault instability behavior induced by coal seam mining. A physical model with the existence of a fault and an extra-thick rock stratum is constructed to simulate the fault movement and calculate relative slip using the methodology. The results indicate that the fault relative slip can be regarded as a dynamic evolution process from local slip to global slip on the fault surface. The movement of surrounding rock masses near the fault experiences three stages, including along vertical downward, parallel to the fault and then approximately perpendicular to the fault. There will be an undamaged zone in the extra-thick rock strata when the mining face is near the fault structure. The collapse and instability of this undamaged zone could induce a violent fault relative slip. In addition, the influence of dip angles on the fault relative slip is also discussed. A formula for risk of fault relative slip is further proposed by fitting the relative displacement curves with different fault dip angles.

3D full-time anisotropic TEM modelling using a mixed BDF2/SAI method

Transient electromagnetic (TEM) data are affected by resistivity anisotropy, which should be considered in 3D modelling. The influence of anisotropy on full-time response is the main focus of this research. For spatial discretisation of an anisotropic model, the mimetic finite volume approach was applied. The accuracy of the shift-and-invert (SAI) Krylov subspace approach and the two-step backward differentiation formula (BDF2) for modelling 3D full-time electromagnetic data has been demonstrated. However, both algorithms require time-consuming calculations. The SAI technique requires a number of projection subspace constructions, whereas the BDF2 algorithm necessitates numerous coefficient matrix decompositions. We proposed a novel mixed BDF2/SAI algorithm in this paper, which combines the advantages of the two algorithms. The on-time response is computed using BDF2, while the off-time response is computed using the SAI-Krylov subspace method. The forward results of a 1D model with a half-sine waveform demonstrated that the new algorithm is accurate and faster than both the BDF2 algorithm and the SAI algorithm. During the full-time period, the forward results of a 3D anisotropic model with half-sine waveform show that abnormal responses can be induced. It was shown that the relative abnormal of ${{{\bf b}}_{\boldsymbol{z}}}$ is higher during the on-time period, while the relative abnormal of $\partial {{{\bf b}}_{\boldsymbol{z}}}/\partial t$ is higher during the off-time period. Furthermore, the change in relative anomaly is more obvious as the anisotropic block rotates around the x-axis. And the larger the rotation angle, the larger the relative anomaly.

Calculation of matrix permeability from velocity and attenuation of ultrasonic S-wave

Previously, hydrogeologists and petroleum engineers use seepage experiments to measure permeability. This paper develops a novel method to calculate matrix permeability from velocity and attenuation of an ultrasonic S-wave. At first, permeability is derived as a function of frequency when an S-wave scans a fluid-saturated rock. Substituting the permeability into a previous S-wave model gives theoretical velocity and attenuation, in which the nexus parameter is the average distance of aperture representing pores. Fitting the predicted velocity and quality factor against the measured counterparts yields permeability in the full frequency range. For Berea sandstone, the inverted permeability at low frequency (0.0376 Darcy) is comparable to Darcy permeability (0.075 Darcy), confirming that Berea sandstone is homogenous. For Boise sandstone, the inverted permeability at low frequency is 0.0457 Darcy, much lower than Darcy permeability (1 Darcy). When S-wave scans the rocks, its velocity and attenuation are dominated by matrix pore throats and the inverted permeability represents matrix permeability. Unlike Berea sandstone, Boise sandstone has fractures and widely distributed grain diameters. The fractures and the large pores (due to large grain diameter) are preferential pathways that increase Darcy permeability far more than matrix permeability.