Sonic Travel Time Research Articles

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

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.

Statistical feature engineering using discrete wavelet transform: a tool for improving the prediction of compressional and shear sonic travel time logs

This paper aims to predict compressional and shear sonic travel time logs using conventional logs that are easier and less expensive to acquire during the development cycle. The study uses a gradient boosting algorithm and incorporates a novel method of using statistical feature engineering with wavelet transform to improve prediction accuracy across thin beds in hydrocarbon reservoirs. The approach uses a discrete wavelet transform over the neutron log, a prominent feature, to identify thin layers and enhance prediction accuracy. The detailed coefficients are analyzed using Daubechies and Haar wavelets to reconstruct newly transformed data with an enhanced thin-layer signal. The Haar wavelet with three levels is the most optimum wavelet and decomposition levels. The algorithm for the reconstructed log shows an increased thin layer resolution, with an accuracy improvement of 6.8% for the prediction. The proposed approach significantly contributes to geologists, geophysicists, and reservoir technologists for reservoir characterization and safe drilling operations.

A machine learning approach for the prediction of pore pressure using well log data of Hikurangi Tuaheni Zone of IODP Expedition 372, New Zealand

Pore pressure (PP) information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development. PP prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design, and mud weight estimation for wellbore stability. However, the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting, where sedimentation rate is relatively high and PP can be driven by various complex geo-processes. To overcome these difficulties, an advanced machine learning (ML) tool is implemented in combination with empirical methods. The empirical method for PP prediction is comprised of data pre-processing and model establishment stage. Eaton's method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition 372. Gamma-ray, sonic travel time, bulk density and sonic derived porosity are extracted from well log data for the theoretical framework construction. The normal compaction trend (NCT) curve analysis is used to check the optimum fitting of the low permeable zone data. The statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model development. The dataset is prepared and divided into two parts: Training and Testing. The PP data and well log of borehole U1517A is pre-processed to scale in between [-1, +1] to fit into the input range of the non-linear activation/transfer function of the decision tree regression model. The Decision Tree Regression (DTR) algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.

Neural Network Model for Permeability Prediction from Reservoir Well Logs

The estimation of the formation permeability is considered a vital process in assessing reservoir deliverability. The prediction of such a rock property with the use of the minimum number of inputs is mandatory. In general, porosity and permeability are independent rock petrophysical properties. Despite these observations, theoretical relationships have been proposed, such as that by the Kozeny–Carmen theory. This theory, however, treats a highly complex porous medium in a very simple manner. Hence, this study proposes a comprehensive ANN model based on the back propagation learning algorithm using the FORTRAN language to predict the formation permeability from available well logs. The proposed ANN model uses a weight visualization curve technique to optimize the number of hidden neurons and layers. Approximately 500 core data points were collected to generate the model. These data, including gamma ray, sonic travel time, and bulk density, were collected from numerous wells drilled in the Western Desert and Gulf areas of Egypt. The results show that in order to predict the permeability accurately, the data set must be divided into 60% for training, 20% for testing, and 20% for validation with 25 neurons. The results yielded a correlation coefficient (R2) of 98% for the training and 96.5% for the testing, with an average absolute percent relative error (AAPRE) of 2.4%. To validate the ANN model, two published correlations (i.e., the dual water and Timur’s models) for calculating permeability were used to achieve the target. In addition, the results show that the ANN model had the lowest mean square error (MSE) of 0.035 and AAPRE of 0.024, while the dual water model yielded the highest MSE of 0.84 and APPRE of 0.645 compared to the core data. These results indicate that the proposed ANN model is robust and has strong capability of predicting the rock permeability using the minimum number of wireline log data.

Looking into the Reservoir: Performance Evaluation of Fractured Long Horizontal Wells Using of Borehole Imaging Logs

Field G is a highly fractured and faulty field, this paper is seeking about the necessity to keep running Image logs in new oil wells and how the interpretation of the results would led the team to completing wells optimally and to design our well completion properly to prevent high initial water cut. Learnings are summarized from 40+ image logs ran in this field over the last few years. A cross field G seismic interpretation confirmed on lapping reservoirs and 3 major fault trends with different transmissibility. Also, sseismic and sub seismic Faults and fractures are confirmed from BHI with a NE-SW strike of field G and can be possibly related to production behavior of some wells. In 2015, field G Water Encroachment Study was conducted to identify the cause of high initial water cut of producers. It proposed that detecting fractures early via under-balanced drilling, Borehole Imaging Tools (BHI) and sealing the conduits with production packers would help reduce initial water-cuts [2]. BHI tools are open hole tools that measure either electrical conductivity of the borehole wall or the sonic travel time and amplitude. Micro-conductivity or amplitude and travel time measurements generate an image that shows bedding, fractures and many other fine-scale features. Acoustic tools are preferred in Oil Based Mud (OBM) system and hence they have not been used in field G to the extent that it required a dedicated discussion.

Estimation of Mechanical Rock Properties from Laboratory and Wireline Measurements for Sandstone Reservoirs

Mechanical rock properties are essential to minimize many well problems during drilling and production operations. While these properties are crucial in designing optimum mud weights during drilling operations, they are also necessary to reduce the sanding risk during production operations. This study has been conducted on the Zubair sandstone reservoir, located in the south of Iraq. The primary purpose of this study is to develop a set of empirical correlations that can be used to estimate the mechanical rock properties of sandstone reservoirs. The correlations are established using laboratory (static) measurements and well logging (dynamic) data. The results support the evidence that porosity and sonic travel time are consistent indexes in determining the mechanical rock properties. Four correlations have been developed in this study which are static Young’s modulus, uniaxial compressive strength, internal friction angle, and static Poisson’s ratio with high performance capacity (determination coefficient of 0.79, 0.91, 0.73, and 0.78, respectively). Compared with previous correlations, the current local correlations are well-matched in determining the actual rock mechanical properties. Continuous profiles of borehole-rock mechanical properties of the upper sand unit are then constructed to predict the sand production risk. The ratio of shear modulus to bulk compressibility (G/Cb) as well as rock strength are being used as the threshold criterion to determine the sanding risks. The results showed that sanding risk or rock failure occurs when the rock strength is less than 7250 psi (50 MPa) and the ratio of G/Cb is less than 0.8*1012 psi2. This study presents a set of empirical correlations which are fewer effective costs for applications related to reservoir geomechanics.

Characterization and estimation of gas-bearing properties of Devonian coals using well log data from five Illizi Basin wells (Algeria)

In Algeria, wells drilled in the Illizi Basin suggest the presence of a significant areal trend of Devonian coal seams with the thickest coal seams penetrated in the Lower Devonian stratigraphic unit F6. This makes them some of the oldest thick coal seams encountered. These coals exist between approximately 1500 and 4000 meters below surface. In particular, numerous coals in these formations drilled in the Oudoume field have recorded gas shows while drilling. A study of basic well log data from five wells penetrating Illizi Basin coals is conducted to characterize their distribution and provisionally evaluate their gas-bearing potential using petrophysical analysis coupled with machine learning. A simple multi-layer perceptron model (one hidden layer with four nodes) is used in a novel way to replicate estimates of gas saturation in the coal samples calculated approximately with the modified Kim equation. It does so by considering three commonly measured well-log variables: gamma ray, sonic travel time, deep resistivity (307 data records from the five wells studied). The log-calculated approximations (modified Kim equation) can be matched to better than plus or minus 1 scf/ton by the multi-layer perceptron model. The results and analysis presented provide preliminary encouragement that suggests the presence of a potentially extensive gas-bearing Devonian coal trend in the Illizi Basin that is worthy of further exploration. Future work is required to integrate data from additional wells and laboratory analysis of core samples to verify the extent of that coal trend and to quantify its gas concentrations. Cited as : Baouche, R., Wood, D.A. Characterization and estimation of gas-bearing properties of Devonian coals using well log data from five Illizi Basin wells (Algeria). Advances in Geo-Energy Research, 2020, 4(4), 356-371, doi: 10.46690/ager.2020.04.03

Log data-driven model and feature ranking for water saturation prediction using machine learning approach

Log-based reservoir characterization is one of the widely used techniques to estimate the reservoir properties and make decisions about future plans for hydrocarbon production. Use of the machine learning tools is becoming a more accessible approach for predictive model development in oil and gas engineering. The objective of this research is to identify and rank the most contributing log variables for estimation of water saturation using the machine learning tools. The multilayer perception artificial neural network (MLP-ANN) and kernel function-based least-squares support vector machine (LS-SVM) techniques are employed to develop predictive models for water saturation. The model can capture the non-linear behaviors and high-dimensional complex relationships among field log data variables. Based on the prediction performance of the models, the Levenberg-Marquardt algorithm-based MLP-ANN and the radial kernel function-based LS-SVM model optimized with coupled simulated annealing optimization technique were found to perform better compared to other models. The MLP-ANN and radial kernel function-based LS-SVM approaches lead to the same feature ranking of predictor variables. It was found that the significance order (higher to lower) of influential log variables are the true resistivity, bulk density, neutron porosity, photoelectric factor, gamma-ray, and sonic travel time based on their relative contribution to the water saturation. The strategy introduced in this study assists to forecast water saturation with a relatively few number of log variables, and thus, reduces the number of necessary logs to run during exploration stage, considerably lowering the exploration costs.

Dynamic data driven sonic well log model for formation evaluation

The lack of acoustic measurements places severe limitations on the application of well log data to analyze rock physics. In such conditions, other petrophysical data can be used to predict the shear and compressional sonic travel time. This study presents a novel data-driven model based on a nonlinear autoregressive neural network with exogenous (NARX) input to estimate the shear and compressional sonic travel time due to its ability to accurately determine nonlinearity in sequential and temporal data. The architecture of the model comprises three-layers and ten hidden neurons with gamma ray log as exogenous input. The proposed NARX methodology is developed using 11 wells, six from the Norwegian continental shelf and five from West Africa. The results show that the wells provide sufficiently accurate predictions of the actual sonic well logs using the NARX model. The predicted sonic logs are used to estimate formation property parameters like sonic ratio, sonic difference, sonic porosity, and Poisson's ratio. This paper proves NARX is an affordable, efficient and accurate means to reproduce sonic well logs for formation evaluation.

Application of Artificial Intelligence Techniques to Estimate the Static Poisson's Ratio Based on Wireline Log Data

Static Poisson's ratio (νstatic) is a key factor in determine the in-situ stresses in the reservoir section. νstatic is used to calculate the minimum horizontal stress which will affect the design of the optimum mud widow and the density of cement slurry while drilling. In addition, it also affects the design of the casing setting depth. νstatic is very important for field development and the incorrect estimation of it may lead to heavy investment decisions. νstatic can be measured in the lab using a real reservoir cores. The laboratory measurements of νstatic will take long time and also will increase the overall cost. The goal of this study is to develop accurate models for predicting νstatic for carbonate reservoirs based on wireline log data using artificial intelligence (AI) techniques. More than 610 core and log data points from carbonate reservoirs were used to train and validate the AI models. The more accurate AI model will be used to generate a new correlation for calculating the νstatic. The developed artificial neural network (ANN) model yielded more accurate results for estimating νstatic based on log data; sonic travel times and bulk density compared to adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) methods. The developed empirical equation for νstatic gave a coefficient of determination (R2) of 0.97 and an average absolute percentage error (AAPE) of 1.13%. The developed technique will help geomechanical engineers to estimate a complete trend of νstatic without the need for coring and laboratory work and hence will reduce the overall cost of the well.

Development of New Mathematical Model for Compressional and Shear Sonic Times from Wireline Log Data Using Artificial Intelligence Neural Networks (White Box)

Compressional (P-wave) and shear (S-wave) velocities are used to estimate the dynamic geomechanical properties including: Poisson’s ratio, Young’s modulus, and Lame parameters. These parameters are mainly used in estimating the static properties of the formation rocks as well as the in situ stresses. The sonic logs are not always available, epically for old wellbores. Also, in several occasions when the sonic logs are available, missing sections found in the well logs might affect the analysis results. To the authors’ knowledge, there is no single straightforward correlation that can be used to accurately estimate both P- and S-wave travel times directly from the well log data. Most of the existing correlations use the P-wave velocity to measure the S-wave velocity. The main purpose of this study is to develop accurate and simple empirical models using wireline log data (bulk density, gamma ray, and neutron porosity) to predict the sonic travel times (P-wave and S-wave). These wireline logs are slandered wireline log data that are commonly recorded in most of the wells. Three robust artificial intelligence techniques, namely: support vector machine (SVM), artificial neural network (ANN), and adaptive neurofuzzy interference systems (ANFIS), were employed and compared based on their prediction performance. Ultimately, using the weights and biases of optimized ANN model, a simple generalized empirical correlation is derived that can be used without the need of costly commercial software’s to run the AI models. The obtained results showed that ANN, ANFIS, and SVM can be used to estimate P-wave and S-wave travel times. ANN outperformed the ANFIS and SVM by yielding the lowest average absolute percentage error (AAPE) and the highest coefficient of determination $$\left( R^{2}\right) $$ for predicting P-wave and S-wave travel times. ANN model could predict the P-wave and S-wave travel times from wireline log data with high accuracy giving $$R^{2}$$ of 0.98 when compared to actual field data. In addition, the developed empirical correlations prediction completely matched the ANNs prediction. The AAPE of the predicted P and S-waves travel times was less than 5%. The developed correlations are very accurate and can help geomechanical engineers to determine the dynamic geomechanical properties (Poisson’s ratio and Young’s modulus) and propose any operation in case where sonic logs are missing.

Prediction of Compressional Wave Velocity Using Regression and Neural Network Modeling and Estimation of Stress Orientation in Bokaro Coalfield, India

Velocity of compressional wave (V P) of coal and non-coal lithology is predicted from five wells from the Bokaro coalfield (CF), India. Shear sonic travel time logs are not recorded for all wells under the study area. Shear wave velocity (Vs) is available only for two wells: one from east and other from west Bokaro CF. The major lithologies of this CF are dominated by coal, shaly coal of Barakar formation. This paper focuses on the (a) relationship between Vp and Vs, (b) prediction of Vp using regression and neural network modeling and (c) estimation of maximum horizontal stress from image log. Coal characterizes with low acoustic impedance (AI) as compared to the overlying and underlying strata. The cross-plot between AI and Vp/Vs is able to identify coal, shaly coal, shale and sandstone from wells in Bokaro CF. The relationship between Vp and Vs is obtained with excellent goodness of fit (R 2) ranging from 0.90 to 0.93. Linear multiple regression and multi-layered feed-forward neural network (MLFN) models are developed for prediction Vp from two wells using four input log parameters: gamma ray, resistivity, bulk density and neutron porosity. Regression model predicted Vp shows poor fit (from R 2 = 0.28) to good fit (R 2 = 0.79) with the observed velocity. MLFN model predicted Vp indicates satisfactory to good R2 values varying from 0.62 to 0.92 with the observed velocity. Maximum horizontal stress orientation from a well at west Bokaro CF is studied from Formation Micro-Imager (FMI) log. Breakouts and drilling-induced fractures (DIFs) are identified from the FMI log. Breakout length of 4.5 m is oriented towards N60°W whereas the orientation of DIFs for a cumulative length of 26.5 m is varying from N15°E to N35°E. The mean maximum horizontal stress in this CF is towards N28°E.

Identification of the coal structure and prediction of the fracturability in the No. 8 coal reservoir, Gujiao block, China

Five boreholes were selected in the Gujiao block, Xishan, Taiyuan, China, as calibration wells to identify the coal structure. The geophysical-log responses of the coal structure in the No. 8 coal reservoir, Gujiao block, were investigated using coal-core logging combined with actual observations in the borehole profile of the coal mine. Natural gamma, density, apparent resistivity, and sonic travel time logs were extracted at 0.15 m intervals from 41 undeformed coal, 29 cataclastic coal, and 48 granulated coal regions. Box plots and Fisher’s maximum separation criterion were used to screen and identify sensitive log responses of the coal structure. The coal structure was identified in 31 boreholes using the available logs in the block, and the layered distribution patterns of the coal structure were discussed. The fracturability of the coal structure was divided into types using cluster analysis based on the thickness ratio of the coal structure and the relationship between the coal structure and its permeability. The results show that sensitive log responses for identifying undeformed coal and cataclastic coal are natural gamma, followed by density and apparent resistivity; log responses for identifying cataclastic coal and granulated coal-mylonitized coal are sonic travel time, apparent resistivity and natural gamma. The sensitive log response data were integrated and coal structure identification models were constructed based on the principle of amplifying the log responses to identify the coal structure in the No. 8 coal reservoir. The reservoir generally contains two or three dirt bands, and the coal structure is divided into several independent layers, with the cataclastic coal and granulated coal-mylonitized coal distributed in the middle of the reservoir. The coal structure was classified into four types and four subtypes through cluster analysis of the boreholes. Under the control of the Malan syncline, type I and type II are developed in the No. 8 coal reservoir in the southern part of the study area and in the east and north wings of the Malan syncline; they have good fracturability. Type III and type IV are mainly present in the No. 8 coal reservoir at the synclinal axis; they have poor fracturability. For type IV dominated by granulated coal, it is difficult to improve the reservoir permeability by fracturing; therefore, other strengthened permeability-improving measures should be considered.

Application of Artificial Neural Networks in Prediction of Uniaxial Compressive Strength of Rocks using Well Logs and Drilling Data

It is critical to obtain the rock strength along the wellbore to control drilling problems such as pipe sticking, tight hole, collapse and sand production. The purpose of this research is to predict the uniaxial compressive strength based on data of sonic travel time, formation porosity, density and penetration rate. For prediction of UCS, artificial neural networks were developed between UCS and input data resulting a practical correlation. In this research, a long well segment possessing complete and continuous data coverage has been analysed, and collected data of the wellbore are used to correlate data of the four mentioned input parameters of artificial neural networks with uniaxial compressive strength data as network targets. Selection of input parameters is based on a vast literature review in this area. Due to the fact that standard experimental test methods based on established standards require costly equipment and that the methods for sample preparation is difficult and time-consuming, indirect methods are more favourable. Using these methods, the UCS values are predicted in a simpler, faster and more economical way. In this study, it is concluded that artificial neural networks are a good predictor of rock strength, and can reduce drilling costs significantly. It is observed in this paper that UCS predicted values by neural networks are very close with lab and field data, which is concluded by analysis of network performance results including mean squared error and correlation coefficient. It is also concluded in this study that input parameters which are chosen in this study, have deep effects in UCS prediction studies, and should be considered in other scientific studies. Conclusions show that using artificial neural networks to predict UCS of formation rocks in petroleum fields around the world, would ease UCS estimation, optimize drilling plans and decrease costs.

Mechanical characteristics of laminated sand–shale sequences identified from sonic velocity and density correlations

In the past, numerous relations between sonic velocity (i.e. an inverse of sonic travel time DT) and rock density were developed that were suitable for certain fields or certain rocks only. In this paper, the correlations between sonic and density log measurements were investigated again using the data information from multiple fields of Gulf of Mexico (GOM) and North Sea (NS). For sandstone-shale sequences, the dominant linear relationships between sonic travel time (DT) and formation density (RHOB) were observed, and compared with the popular Gardner’s method (Gardner et al. in Geographics 39(6):770–780, 1974) along with other known methods. The implications of data clusters distinguished by formation lithologies and rock mechanical strengths were revealed from cross-plot analysis. The probabilities and uncertainties of the developed correlations were determined using the actual histograms from the collected data of GOM and NS fields coupled with Monte Carlo simulations.

Prediction of Rock Strength using Drilling Data and Sonic Logs

Uniaxial test (also called unconfined compression test) is one of most important tests used to measure rock strength. It is critical to obtain the rock strength parameters along the wellbore. Awareness of rock strength could better control drilling problems such as pipe sticking, tight hole, collapse, pack off and sand production. Rock strength also controls the drilling rate of penetration (ROP). The calculations of uniaxial Compressive Strength (UCS) are based on a simplified version of the ROP model proposed for tricone bits. The purpose of this research was to predict the uniaxial compressive strength based on sonic logs as a function of sonic travel and formation porosity. For obtaining continuous log strength along the wellbore in Ahwaz oilfield, quantitative relationships were developed between UCS and sonic travel time as well as UCS and both of sonic travel time and formation porosity. They actually are the outcome of regression analysis that resulting relationships are very practical and most often are specified by the researcher to the region of study. In this work, a larger well segment has been analyzed which there is no information break throughout the segment and it is investigated continuously. Using this approach, 3D model was a better predictor of uniaxial compressive strength than obtained equations related to transient time and results have also confirmed that the use of an recommended equations in this study can reduce drilling costs significantly.

Advanced Workflow Package for Shale Assets Developed

Technology Update The growth in unconventional resource plays in the past several years has produced a burgeoning need for new software tools for organic shales. Geoscientists need tools to help them understand complex hydrocarbon generation, storage capacity, and migration paths in source rock reservoirs, enabling them to flag and map optimized pay. Engineers need tools to help them define optimum techniques to deliver the most shale gas and oil to the market and enable them to build the best reservoir models to exploit these resources. And for unconventional resource development to proceed as it should, these tools must work together in a common framework. An advanced integrated petrophysical evaluation software package, based on a calibrated workflow, was recently developed by Halliburton for organic shales. The concept behind it was to bring all the requisite pieces of an exploration shale play analysis into a single vantage point for an asset team. This is critical when very few vertical exploration wells are used to define the economics of these resource plays before full-scale horizontal development begins. The software’s workflow modules encompass the following capabilities: total organic carbon (TOC) and organic maturity estimation; fluid and minerals evaluation; advanced saturation modeling; mechanical properties and brittleness; 3D stress and stress orientation; permeability; and pay analysis. TOC Estimation and Organic Maturity To define the resource volume, one needs to determine an accurate volume of organic kerogen present in the rock. To determine potential hydrocarbon type, the level of thermal maturity must be established. To solve for kerogen, the TOC measured by core pyrolysis can be calibrated to logs, using eight industry accepted correlations. Organic maturity, VRo, is measured by actual vitrinite reflectance or calculated from pyrolysis-derived Tmax (temperature between 300°C and 600°C that generates peak hydrocarbons from existing kerogen). This maturity value is used to make the final TOC calibration and predict hydrocarbon type. Fluid and Minerals Evaluation The heart of the volumetric analysis is its probabilistic solver. Total porosity in organic shales can only be resolved by logs when relative amounts of geochemically derived minerals are measured and combined with the TOC calculation. Minimum requirements for this type of analysis include a triple combo log, neutron capture spectroscopy, and natural gamma spectroscopy. The software uses a probabilistic error minimization methodology to determine formation fluid and mineral volumes. The idea is to construct theoretical logs that closely replicate actual logs. Tool response equations are expressed in terms of fluid and mineral volumes and their corresponding tool response parameters. Most response equations are linear. Some, such as neutron, conductivity, and certain acoustic equations, are nonlinear. The inclusion of additional evaluation tools, such as the dipole sonic travel time curves DTC (compressional velocity) and DTS (shear velocity), helps add coherence to the analysis, as long as the correct acoustic equations are used for harder rock-clay shales.

Prediction of hydraulically transmissive fractures using geological and geophysical attributes: a case history from the mid Jhuoshuei River basin, Taiwan

The feasibility of using geological and geophysical well-log and borehole-televiewer data for the identification of hydraulically transmissive fractures is evaluated. Twenty-nine boreholes were drilled to a depth of 100 m in the middle-stream basin of Jhuoshuei River, Taiwan. Four criteria that assist in indicating the potential presence of permeable zones are proposed, including: lower gamma-ray response compared with the average response, divergence of the short normal-resistivity log relative to that of the long one, longer sonic travel time, and the appearance of discernible openings detected with the televiewer. With these, the transmissivities at the predetermined depths were estimated and verified by an in-situ hydraulic test. The statistical results indicate that, particularly in the mountainous area where a complex folded structure with a succession of synclines and anticlines is shown, the interpretation of lithologic conditions is not necessary to identify the presence of a relatively higher-permeability zone. Comparatively, the estimates of porosity and fracture aperture are the necessary premises to the prediction of hydraulically transmissive fractures. A joint consideration of all four criteria is found, allowing a less biased evaluation of the fracture transmissivity.

In situ estimation of roof rock strength using sonic logging

Sonic travel time logging of exploration boreholes is routinely used in Australia to obtain estimates of coal mine roof rock strength. Because sonic velocity logs are relatively inexpensive and easy to obtain during exploration, the technique has provided Australian underground coal mines with an abundance of rock strength data for use in all aspects of ground control design. However, the technique depends upon reliable correlations between the uniaxial compressive strength (UCS) and the sonic velocity. This paper describes research recently conducted by NIOSH aimed at developing a correlation for use by the U.S. mining industry. From two coreholes in Illinois, two from Pennsylvania, and one each from Colorado, western Kentucky and southern West Virginia, sonic velocity logs were compared with UCS values derived from Point Load tests for a broad range of coal measure rock types. For the entire data set, the relationship between UCS and sonic travel time is expressed by an exponential equation relating the UCS in psi to the travel time of the P-wave in μs/ft. The coefficient of determination or R-squared for this equation is 0.72, indicating that a relatively high reliability can be achieved with this technique. The strength estimates obtained from the correlation equation may be used to help design roof support systems. The paper also addresses the steps that are necessary to ensure that high-quality sonic logs are obtained for use in estimating UCS.

Probability distributions for locations of calling animals, receivers, sound speeds, winds, and data from travel time differences

A new nonlinear sequential Monte Carlo technique is used to estimate posterior probability distributions for the location of a calling animal, the locations of acoustic receivers, sound speeds, winds, and the differences in sonic travel time between pairs of receivers from measurements of those differences, while adopting realistic prior distributions of the variables. Other algorithms in the literature appear to be too inefficient to yield distributions for this large number of variables (up to 41) without recourse to a linear approximation. The new technique overcomes the computational inefficiency of other algorithms because it does not sequentially propagate the joint probability distribution of the variables between adjacent data. Instead, the lower and upper bounds of the distributions are propagated. The technique is applied to commonly encountered problems that were previously intractable such as estimating how accurately sound speed and poorly known initial locations of receivers can be estimated from the differences in sonic travel time from calling animals, while explicitly modeling distributions of all the variables in the problem. In both cases, the new technique yields one or two orders of magnitude improvements compared with initial uncertainties. The technique is suitable for accurately estimating receiver locations from animal calls.