Compressional Slowness Research Articles

Multiscale Assessment of Caprock Integrity for Geologic Carbon Storage in the Pennsylvanian Farnsworth Unit, Texas, USA

Leakage pathways through caprock lithologies for underground storage of CO2 and/or enhanced oil recovery (EOR) include intrusion into nano-pore mudstones, flow within fractures and faults, and larger-scale sedimentary heterogeneity (e.g., stacked channel deposits). To assess multiscale sealing integrity of the caprock system that overlies the Morrow B sandstone reservoir, Farnsworth Unit (FWU), Texas, USA, we combine pore-to-core observations, laboratory testing, well logging results, and noble gas analysis. A cluster analysis combining gamma ray, compressional slowness, and other logs was combined with caliper responses and triaxial rock mechanics testing to define eleven lithologic classes across the upper Morrow shale and Thirteen Finger limestone caprock units, with estimations of dynamic elastic moduli and fracture breakdown pressures (minimum horizontal stress gradients) for each class. Mercury porosimetry determinations of CO2 column heights in sealing formations yield values exceeding reservoir height. Noble gas profiles provide a “geologic time-integrated” assessment of fluid flow across the reservoir-caprock system, with Morrow B reservoir measurements consistent with decades-long EOR water-flooding, and upper Morrow shale and lower Thirteen Finger limestone values being consistent with long-term geohydrologic isolation. Together, these data suggest an excellent sealing capacity for the FWU and provide limits for injection pressure increases accompanying carbon storage activities.

Geomechanical model and wellbore stability analysis utilizing acoustic impedance and reflection coefficient in a carbonate reservoir

One of the most important oil and gas drilling studies is wellbore stability analysis. The purpose of this research is to investigate wellbore stability from a different perspective. To begin, vertical stress and pore pressure were calculated. The lowest and maximum horizontal stress were calculated using poroelastic equations. The strike-slip to normal fault regime was shown by calculated in situ stress values. The 1-D geomechanical model was utilized to investigate the failure mechanisms and safe mud window estimation using the Mohr–Coulomb failure criterion. Using density and sonic (compressional and shear slowness) logs, the acoustic impedance (AI) and reflection coefficient (RC) logs were determined subsequently. The combination of layers with different AI indicates positive and negative values for the RC, zones prone to shear failure or breakout, and the mud weight in these zones should be increased, according to the interpretation of the AI and RC readings and the results of the geomechanical model. Furthermore, the zones with almost constant values of AI log and values close to zero for RC log are stable as homogeneously lithologically, but have a lower tensile failure threshold than the intervals that are sensitive to shear failure, and if the mud weight increases, these zones are susceptible to tensile failure or breakdown. Increased porosity values, which directly correspond with the shear failure threshold and inversely with the tensile failure threshold, cause AI values to decrease in homogenous zones, but have no effect on the behavior of the RC log. This approach can determine the potential zones to kick, loss, shear failure, and tensile failure in a short time.

Predicting the Rock Sonic Logs While Drilling by Random Forest and Decision Tree-Based Algorithms

AbstractThe sonic data provide significant rock properties that are commonly used for designing the operational programs for drilling, rock fracturing, and development operations. The conventional methods for acquiring the rock sonic data in terms of compressional and shear slowness (ΔTc and ΔTs) are considered costly and time-consuming operations. The target of this paper is to propose machine learning models for predicting the sonic logs from the drilling data in real-time. Decision tree (DT) and random forest (RF) were employed as train-based algorithms for building the sonic prediction models for drilling complex lithology rocks that have limestone, sandstone, shale, and carbonate formations. The input data for the models include the surface drilling parameters to predict the shear and compressional slowness. The study employed data set of 2888 data points for building and testing the model, while another collected 2863 data set was utilized for further validation of the sonic models. Sensitivity investigations were performed for DT and RF models to confirm optimal accuracy. The correlation of coefficient (R) and average absolute percentage error (AAPE) were used to check the models’ accuracy between the actual values and models’ outputs, in addition to the sonic log profiles. The results indicated that the developed sonic models have a high capability for the sonic prediction from the drilling data as the DT model recorded R higher than 0.967 and AAPE less than 2.76% for ΔTc and ΔTs models, while RF showed R higher than 0.991 with AAPE less than 1.07%. The further validation process for the developed models indicated the great results for the sonic prediction and the RF model outperformed DT models as RF showed R higher than 0.986 with AAPE less than 1.12% while DT prediction recorded R greater than 0.93 with AAPE less than 1.95%. The sonic prediction through the developed models will save the cost and time for acquiring the sonic data through the conventional methods and will provide real-time estimation from the drilling parameters.

Comprehensive Rock Physics, Petrophysics workflow to correct Sonic Logs for improved seismic QI: Study using wells of Krishna Godavari basin

Rock physics analysis uses well log data as direct measurement of physical properties of the subsurface formations. Hence, in seismic reservoir characterisation they do not suffer the same level of scrutiny as seismic. Mud-filtrate invasion during drilling contaminates porous formation affecting log responses in non-intuitive ways. Ignoring invasion effect on well logs prior to quantitative petrophysical interpretations may led to erroneous reserve estimates as well as well-to-seismic misties. In addition, sonic logs are prone to ambiguity incorporated by weak waveform generation and non-unique coherence picking. This gives rise to non-unique compressional and shear slowness data. To correct the invasion affected logs, we have designed a workflow that effectively improves the recorded data. This workflow uses modified Gassmann (1951) fluid replacement modelling (FRM) method for sonic data correction. In addition, following an improved coherence picking we get a further updated sonic slowness. Using the corrected data in synthetic seismic generation enables us to achieve substantial improvements in synthetic-seismic correlation values. Furthermore, use of updated data results in a better three way matching with vertical seismic profiling (VSP) data.

Advanced monopole and dipole sonic log data processing — Part 1: Real time

Real-time open-hole wireline sonic logging data processing becomes a nontrivial task to accurately, automatically, and efficiently evaluate the compressional and shear slowness of a borehole rock formation when human interaction is not possible and the signal processing time is limited to the elapsed time between different transmitter excitations. To address real-time sonic data processing challenges, we have developed self-adaptive, data-driven methods to accurately measure the formation P- and S-wave slowness from monopole and dipole waveforms in all types of formations. These new real-time processing techniques take advantage of the fact that advanced wireline sonic logging tools have wide frequency responses and little to no detectable tool body arrivals. These technology improvements provide an opportunity to implement a first-motion-detection technique that detects the onset of P-waves in the monopole array waveforms. The knowledge of compressional arrival time and corresponding slowness are then used to project an appropriate slowness-time window to identify the monopole refracted S-wave and its slowness based on the range of possible compressional to shear velocity ratio ([Formula: see text]/[Formula: see text]) for earth rock formation. To process the borehole dipole flexural waves, we develop a new, data-driven frequency-domain method that enables the evaluation of the full flexural-wave dispersion response and its corresponding low-frequency shear slowness asymptote. Field data processing results indicate that our methods provide high-quality compressional slowness (delta-T compressional) and shear slowness measurements that are not affected by other borehole modes or dispersion complications in most formation types.

Generating pseudo well logs for a part of the upper Bakken using recurrent neural networks

To develop a reservoir, we need to understand the distribution of key reservoir properties. Those formation properties are mostly derived from well log data. However, obtaining well log data is so expensive that the data we acquire is never sufficient. Therefore, we need to create pseudo data from real measured data. One approach is to generate synthetic well logs using recurrent neural networks (RNNs). Though relatively nascent, it is proven that RNNs allow for the generation of well logs with reasonable accuracy at a fraction of the price. Currently, gamma ray is the most cultivated well log in the E&P process today (owing to its decisive role during drilling operations and affordable price). All the same, a more extensive log suite is required to comprehend the producibility and frackability of hydrocarbon reservoirs like shale reservoirs.In this study, an upper Bakken database of 97 wells which contain gamma ray, bulk density, photoelectric index, density porosity, neutron porosity, true resistivity, compressional and shear slowness was used to develop deep learning models apt to creating synthetic logs for another set of 40 wells in the upper Bakken. The latter wells only had measured gamma ray. Using petroleum engineering concepts and RNNs, data-driven models capable of generating the remaining logs were created. The data-driven models were validated using wells with logs that have been removed from the database to serve as blind validation wells. The performance of the models (5–10 Mean Average Percentage Error and R-squared of 0.6–0.8) suggests the workflow described in this study is a viable way to maximize the value of a gamma ray log. With the ability to generate logs of similar quality for all wells in a shale asset (starting only with gamma ray), superior reservoir evaluation and thus improved reservoir management can be achieved.

Micromechanics modelling for mineral volume fraction determination: application on a terrigenous formation

This work presents a non-linear Self-Consistent (SC) micromechanics method to model the observed physical elastic properties of a terrigenous formation with the purpose to obtain its depth mineral volume fractions profile. In this approach, it is first assumed that the observed physical elastic properties obtained from well logs, such as the density rho_{o} and the elastic compressional Vp_{o} and shear Vs_{o} velocities, are a non-linear relationship of the unknown mineral volume fractions alpha. Then, a gradient descent algorithm is implemented to seek for those volume fractions alpha for which differences between modelled and observed physical elastic properties are minimum. It is assumed that quartz, calcite and clay are the main comprising minerals of the formation. Obtained volume fractions profile follow the same general trends to those estimated by implementing the Linear Least-Squares Inversion LLSI method which is widely used in petrophysical analysis to obtain mineral concentrations from density rho_{o}, photoelectric effect Pe_{o} and compressional slowness Delta tp_{o} well logs. Results also show that calcite and clay volume fractions from these two methods are highly correlated while quartz volume fractions show low correlation. Further comparison between clay concentrations from SC method with clay concentrations calculated from direct measurements of gamma ray GR well logs used as a guideline also exhibits high correlation. These results suggest that the SC method is better suited to obtain clay and calcite volume fractions rather than quartz volume fractions. However, SC method can provide with insights about the general distribution of quartz along the borehole.

Lithologically controlled behaviour of the Dorozsma metamorphic hydrocarbon reservoir (Pannonian Basin, SE Hungary)

Numerous fractured hydrocarbon reservoirs occur in the metamorphic basement of the Pannonian Basin in Hungary, and among them, the Dorozsma field is one of the most productive. Here 66 boreholes penetrated the metamorphic massif, making detailed petrographic and microstructural evaluation possible. Well-log data (gamma-ray, density, neutron porosity and compressional slowness) are available for each well, which can be used to extend the petrographic information spatially.The data suggest three basic rock types in the study area, which align along a consequent rock column. In the deepest structural positions, amphibolite is typical, while the top of the basement is dominated by kyanite gneiss. Thermobarometric calculation results prove that the gneiss-rich realm was metamorphosed at a significantly higher temperature than the lowermost amphibolite, suggesting a post-metamorphic structural contact between them. The contact zone, observed in numerous boreholes, contains intensively deformed dolomite marble of a few tens of metres in thickness. The peak metamorphic temperature of this rock type is much lower than those typically seen in the two neighbouring blocks. Localising the position of the dolomite marble horizon in each well was accomplished using petrographic and well-log information, and through this investigation, a flat, low-angle shear zone became visible, cut by a set of younger normal faults. Hydrocarbon production data clearly shows that the best, most productive intervals in each well are strongly correlated to the shear zone between the gneiss- and amphibolite-dominated realms, namely the sheared dolomite marble zone.

Acoustic evidence for a broad, hydraulically active damage zone surrounding the Alpine Fault, New Zealand

We present an analysis of the wireline sonic logs acquired through Alpine Fault hanging wall rocks during the second phase of the Deep Fault Drilling Project (DFDP-2). Due to inaccuracy in the initial tool-processed slowness logs, a more robust reprocessing of the data using the slowness–time coherence method was required. The reprocessing of the data yielded slowness logs that provide a significantly better estimate of compressional and shear slowness. The average slowness values of compressional and shear waves are 68 μs/ft and 110 μs/ft, respectively, corresponding to velocities of 4.5 km/s and 2.8 km/s, with some excursions related to fractures, borehole conditions, and tool settings, but no systematic downhole trends. The log values are compared to laboratory measurements of P- and S-wave velocity for samples of Alpine schist and mylonitic rocks to examine the scale dependence of the measurement. The average log velocity is 2 to 18% slower than the laboratory measurements due to the presence of mesoscale fractures that are present in situ but are not sampled at the laboratory scale. Using effective medium theory, we estimate the crack density in the hanging wall host rock to be ≤0.4. Comparison to field-scale seismic studies indicates that there is a broad (500–600 m wide) outer damage zone surrounding the intensely deformed inner damage zone of the Alpine Fault. This fractured outer zone facilitates fluid flow through the hanging wall and around the Alpine Fault.

A rock physics modelling algorithm for simulating the elastic parameters of shale using well logging data

As a high-resolution geophysical method employed by the oil and gas industry, well logging can be used to accurately investigate reservoirs. Challenges associated with shale gas reservoir exploration increase the importance of applying elastic parameters or velocity at the logging scale. An efficient shale rock physics model is the foundation for the successful application of this method. We propose a procedure for modelling shale rock physics in which an appropriate modelling method is applied for different compositions of shale rock. The stiffnesses of the kerogen and fluid (oil, gas or water) mixture are obtained with the Kuster-Toksöz model, which assumes that the fluid is included in the kerogen matrix. A self-consistent approximation method is used to model clay, where the clay pores are filled with formation water. The Backus averaging model is then used to simulate the influence of laminated clay and laminated kerogen. Elastic parameter simulations using well logging data show the importance of treating the volume fractions of laminated clay and kerogen carefully. A comparison of the measured compressional slowness and modelled compressional slowness shows the efficiency of the proposed modelling procedure.

Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network

Rate of penetration (ROP) enhancement serves as a key factor in reducing drilling time and hence drilling costs. ROP enhancement requires identification of the parameters affecting this rate. However, the large number of effective parameters, which are further immersed in noise, makes it difficult to present a highly accurate and comprehensive model. In the present research, in order to predict the drilling ROP in one of the vertical wells drilled into the Karanj Oilfield, a hybrid model composed of a multilayer perceptron (MLP) neural network together with either a particle swarm optimization (PSO) algorithm or a cuckoo optimization algorithm (COA) was used. For this purpose, first petrophysical logs and drilling data were denoised using the Savitzky–Golay filter. Then, the 'plus-l-take-r' method was used to select superior features. Feature selection results indicated that an increase in the number of input parameters tends to reduce the error associated with the estimator model; however, the error reduction rate was seen to be negligible for models with five or more input parameters. Therefore, five parameters were considered as input parameters in MLP-COA and MLP-PSO hybrid models: rotary speed, weight on bit, shear wave slowness, compressional wave slowness, and flow rate. A comparison of errors and coefficients of determination in the training phase of the two models indicated that MLP-COA model tended to converge way faster and more accurately. The small difference in generated error using this model between training and testing phases indicated the high reliability and generalizability of the model. Comparing the results of the model trained with raw and denoised data against the same set of selected features clearly showed the positive effect of the denoising step on the accuracy of the model. Validation of the proposed model via the multilinear regression method was indicative of the superior performance of the MLP-COA model, so that it could be confidently stipulated that this model can be used to estimate the ROP at other vertical wells near the studied well. Further, provided the required information is available, this method can predict the ROP to high accuracy in vertical oil and gas wells.

Rock heterogeneity at the centimeter scale, proxies for interfacial weakness, and rock strength-stress interplay from downhole ultrasonic measurements

Analysis of data acquired with an ultrasonic rotating device lowered inside a vertical well of a highly laminated, kerogen-rich, carbonate source formation reveals centimeter-scale rock heterogeneities. The high-frequency (50–600 kHz) measurement used consists of a pulse-echo modality that yields a high-resolution (millimeter-scale) borehole shape and a pitch-catch modality with one transmitting and two receiving transducers that provide compressional (P) and shear (S) slownesses (depth-versus-azimuth) images estimated from signals propagating in the near-wellbore region as compressional head waves and pseudo-Rayleigh surface waves. The slownesses are compared with their counterparts estimated from a lower-frequency (1–15 kHz) sonic measurement logged in the same well interval. The sonic P and S logs are seen to average the centimeter-scale slowness spatial variation between compliant and stiff laminates at a scale larger than 30 cm (1 ft). This is also accompanied by a reduction in resolution of the slowness contrast, which markedly reduces the spectrum of rock-mechanical property variations that are estimated from the ultrasonic data. Further, ultrasonic images of the P and S slownesses and borehole acoustic caliper reveal a host of features associated with rock geomechanics in the near-wellbore and that inform on a first-order basis the interplay between rock strength and local stress regime, as affected by the lamination. The features include breakouts present in the stiffer limestone layers and their arrest at the intersection with the compliant siltstone layers, as well as azimuthal P and S slowness variations indicative of azimuthal stress concentrations, but without the appearance of breakouts. The ultrasonic borehole shape data also identify compliant thin layers that retract into the formation by amounts that are commensurate with their Young’s modulus, suggesting a proxy for detecting and characterizing thin-layer weakness in situ.

Petrophysical characteristics and logging evaluation of asphaltene carbonate reservoirs: A case study of the Cambrian Longwangmiao Formation in Anyue gas field, Sichuan Basin, SW China

Abstract The petrophysical and logging response characteristics of asphaltene carbonate reservoirs were examined based on the measurement of porosity and permeability, density, compressional and shear wave slowness, resistivity and Nuclear Magnetic Resonance transverse relaxation time (T2) of cores before and after the bitumen dissolving. The results show that (1) the asphalt can damage the pore structure of the reservoir and cause reduction of effective reservoir space and permeability; (2) with the increase of asphalt content, the compression and shear wave slowness generally decrease while the density and resistivity increase; (3) with the increase of asphalt content, the compressional wave slowness and density change less, while the shear wave slowness and resistivity change larger; and (4) the T2 values of asphalt are generally less than 3 ms, and the higher the maturity of the asphalt, the lower the T2 value. Based on these experiments, a method based on conventional and special logging methods was presented to evaluate asphalt content, effective porosity and water saturation in asphaltene carbonate reservoirs. The method has already been applied to 80 wells in the Longwangmiao Formation of the Anyue gas field in the Sichuan Basin to pick out zones rich in asphalt on the plane, which has effectively guided the selection of well location in the gas reservoir development.

Detection of formation S-wave in a slow formation using a monopole acoustic logging-while-drilling tool

Acoustic logging-while-drilling (LWD) is a technology that is used to measure formation elastic properties during drilling. When the formation shear slowness is smaller than the borehole fluid slowness (i.e., fast formation), monopole logging can be used to obtain formation compressional and shear slownesses by measuring the corresponding refracted waves. In a slow formation in which the shear slowness is larger than the borehole fluid slowness, other logging methods, such as quadrupole LWD, are used instead for shear slowness measurement due to the lack of a fully refracted S-wave. Through modeling analysis, we find that the transmitted S-wave generated by a monopole LWD tool in a slow formation can be detected and used to measure the formation shear slowness. This phenomenon can be explained by Huygens’ principle, which states that every point on a wavefront can be considered as a secondary source that induces particle motion. It is hard to discern the transmitted S-wave in monopole wireline data because it strongly interferes with the Stoneley mode in wireline logging. However, the transmitted S-wave decouples from the Stoneley in the LWD environment because the drill collar slows down the low-frequency part of the Stoneley mode. The nondispersive nature of the transmitted S-wave makes it suitable for shear slowness extraction using time semblance analysis, but sophisticated signal preprocessing might be needed because this wave is generally weak compared with the Stoneley wave. Moreover, our study helps us better understand how the Stoneley mode behaves and interferes with other modes in a slow formation under LWD conditions.

Fast-forward modeling of compressional arrival slowness logs in high-angle and horizontal wells

Compressional (P) and shear (S) sonic propagation modes are routinely measured with modern borehole acoustic logging instruments. Slownesses (inverse of velocity) of P- and S-modes provide information on compressional and shear slownesses of rock formations surrounding the wellbore, respectively. When a high-angle well penetrates horizontal thin layers, the layers function as waveguides for the propagation of sonic waves. Layer-trapped and borehole-guided waves interfere with each other, giving rise to abnormal amplitude variations and phase discontinuity in the time waveforms acquired at receivers. Consequently, waveforms measured across the receiver array often exhibit low semblance, which renders conventional waveform semblance-based processing techniques inadequate for reliable estimation of formation slowness. Instead of calculating waveform semblance, we invoke compressional first-arrival times to describe the propagation properties of the P-mode in high-angle and horizontal (HA/HZ) wells, and we derive P-arrival slownesses from the first-arrival times of the measured waveforms. Because the first-arrival time at a particular receiver is associated with the fastest P-wave, the P-arrival slowness log is much less affected by mode interference than the conventional log obtained using semblance-based processing techniques. Furthermore, numerical tests with high-angle wells indicate that compressional first arrivals are mainly associated with converted P-waves through deviated layers. Accordingly, we introduce a 1D layered simulation method to model P-mode first-arrival times in HA/HZ wells across thinly bedded formations. Synthetic examples confirm that P-arrival slowness logs obtained with our simulation method in HA/HZ wells agree to within 5% with logs obtained using 3D finite-difference simulations. In addition, because the new method simplifies the geometric complexity of the models from 3D to 1D, it saves 99% of CPU time compared with rigorous numerical simulations.

SALSA3D: A Tomographic Model of Compressional Wave Slowness in the Earth’s Mantle for Improved Travel‐Time Prediction and Travel‐Time Prediction Uncertainty

Abstract The task of monitoring the Earth for nuclear explosions relies heavily on seismic data to detect, locate, and characterize suspected nuclear tests. Motivated by the need to locate suspected explosions as accurately and precisely as possible, we developed a tomographic model of the compressional wave slowness in the Earth’s mantle with primary focus on the accuracy and precision of travel‐time predictions for P and Pn ray paths through the model. Path‐dependent travel‐time prediction uncertainties are obtained by computing the full 3D model covariance matrix and then integrating slowness variance and covariance along ray paths from source to receiver. Path‐dependent travel‐time prediction uncertainties reflect the amount of seismic data that was used in tomography with very low values for paths represented by abundant data in the tomographic data set and very high values for paths through portions of the model that were poorly sampled by the tomography data set. The pattern of travel‐time prediction uncertainty is a direct result of the off‐diagonal terms of the model covariance matrix and underscores the importance of incorporating the full model covariance matrix in the determination of travel‐time prediction uncertainty. The computed pattern of uncertainty differs significantly from that of 1D distance‐dependent travel‐time uncertainties computed using traditional methods, which are only appropriate for use with travel times computed through 1D velocity models.

Inversion-based interpretation of borehole sonic measurements using semianalytical spatial sensitivity functions

One of the major challenges in the interpretation of sonic logs is the presence of bed-boundary (shoulder-bed) effects, which can decrease the accuracy of the estimated elastic properties due to spatial averaging caused by the sonic receiver array across thin beds. A reliable approach to account for shoulder-bed effects in the interpretation of sonic logs is the implementation of forward modeling and inversion techniques. We have developed an efficient inversion-based interpretation method for the estimation of in situ rock formation elastic properties from sonic logs acquired in vertical wells. The method uses semianalytical spatial sensitivity functions for fast forward modeling of sonic-slowness logs and estimates formation elastic properties by iteratively matching the available logs with numerical simulations. Due to the intrinsic vertical resolution limits of sonic logs, the inversion method requires predefined bed boundaries using other high-resolution well logs. We first developed a workflow to estimate shear slowness of thin beds from flexural mode dispersions measured at multiple discrete frequencies. Then, we extended the method to estimate layer-by-layer compressional and shear slownesses by combining the slowness logs of nondispersive P- and S-waves. The method was first successfully verified using data numerically simulated for synthetic rock formations consisting of multiple thin beds and exhibiting large elastic property contrasts. Maximum relative errors and maximum uncertainty in the estimates were less than 2% and 6%, respectively. Field data acquired in wells penetrating multiple horizontal thin beds were also used to appraise the reliability and resolution improvement of the method in the estimation of formation compressional and shear slownesses. Our results indicated that our inversion-based interpretation method improved the vertical resolution of sonic logs by 70%. The new inversion-based interpretation method opens up the possibility of integrating modern borehole acoustic measurements with nuclear and resistivity logs for improved poroelastic and petrophysical interpretations.

Broadband electrical properties of clays and shales: Comparative investigations of remolded and preserved samples

The dielectric permittivity of mudrocks cannot be determined from the mixing ratios of the constituent minerals and brine and their individual dielectric response. The high-frequency dielectric permittivity is linked ([Formula: see text] correlation) to water content, but the relationship is complicated by mineral type and the hydration state. The cation exchange capacity (CEC) and specific surface area (SSA) determine the establishment of a polarizable electrical double layer and give rise to the long range diffusion of ions leading to Maxwell-Wagner polarization. This ultimately determines the dielectric response below 50 MHz. Procedures for SSA analysis based on sorption of 2-ethoxyethanol and CEC analysis based on methylene blue titration, were developed for investigating swelling clays. These were combined with a procedure for preparing remolded paste samples from pulverized rock and drill cuttings to form the basis of this investigation. In a case study of 123 drill cutting samples retrieved from an undisclosed well, dielectric analysis of the remolded samples exhibits [Formula: see text] correlation with SSA, [Formula: see text] correlation with CEC, and [Formula: see text] correlation with clay content. A pseudodielectric log of the remolded drill cuttings exhibited a weak correlation with downhole resistivity, gamma ray, and compressional slowness logs measured by a service company in the shaly sections of the well, but no correlation in the sandy section of the well. This attributed to better preservation of shaly cuttings due to finer microstructure and therefore less alteration during uphole transport and sample preparation.

Prediction of Compressional and Shear Slowness from Conventional Well Log Data: Using Intelligent Systems

Compressional slowness (DTCO) and shear slowness (DTSM) are two fundamental parameters that have many applications in petrophysical, geophysical, and geomechanical operations. These two parameters can be obtained using a dipole sonic imaging tool, but unfortunately this tool is run in just a few wells of a field. Therefore, it is important to predict compressional and shear slowness indirectly from the other conventional well logs that have strong correlation with these two parameters when the dipole sonic log is missing in a given well. The overriding impetus of this work is to construct intelligent systems, including artificial neural network, fuzzy logic, and adaptive neuro fuzzy inference system, for prediction of compressional and shear slowness. Finally, the results are combined with a committee machine algorithm using a simple averaging technique. A total number of 2,715 data points from Asmari formation, which has compressional and shear slowness, are used. These data are divided into two groups: 2,172 data points for construction of intelligent systems and 543 data points used for model testing. The results showed that, despite a difference in concept, all of the intelligent techniques were successful for estimation of compressional and shear slowness.

A New Approach for Compressional Slowness Modeling Using Wavelet Coefficients

Compressional slowness, which may be derived from sonic log, is an important parameter that can be used for determining physical rock properties, such as young modulus and Poisson's ratio. Since the sonic logs are not common in oil fields, modeling the compressional slowness indirectly seems to be a key approach in obtaining the required data for calculating mechanical properties. In this study, a new approach is introduced to construct synthetic models of sonic logs using wavelet coefficients and artificial neural network. Obtained results confirm the applicability of this model in sonic log prediction.