Sonic Logs Research Articles

The Prediction of Coalbed Methane Layer in Multiple Coal Seam Groups Based on an Optimized XGBoost Model

The prediction of the optimal coalbed methane (CBM) layer plays a significant role in the efficient development of CBM in multiple coal seam groups. In this article, the XGBoost model optimized by the tree-structured Parzen estimator (TPE) algorithm was established to automatically predict the optimal CBM layer in complex multi-coal seams of the Dahebian block in Guizhou Province, China. The research results indicate that the TPE XGBoost model has higher evaluation metrics than traditional machine learning models, with higher accuracy and generalization ability. The optimal coalbed methane layer predicted by the model for the Dacong 1–3 well is the 11th coal seam. In addition, the interpretation results of the model indicate that sonic (AC) and caliper logging (CAL) are relatively important in determining the optimal CBM layer. The favorable layers for coalbed methane development are distributed in coal seams with developed fractures and high gas content. The TPE-XGBoost model can help us objectively analyze the significance of different types of logging, quickly predict the optimal layer in complex multiple coal seam groups, and greatly reduce costs and subjective impact. It provides a new approach to predict the best CBM layer in multiple coal seam groups in the Guizhou Province in the southwest of China.

In situ static elastic properties assessment and validation with pressuremeter testing using a formation tester tool

Pressuremeter testing (PMT) is a formation test that consists of inflating a cylindrical packer inside a borehole while measuring the radial deformation or injected fluid volume as a function of packer pressure. Provided that the stiffness of the packer measuring system is known and large enough compared to that of the formation, changes in packer pressure associated with changes in injected fluid volume provide a direct measurement of formation stiffness. In turn, the in situ static shear modulus is obtained from the formation stiffness at a length scale similar to that of the packer. Here, we report on the first field-scale campaign of PMTs in deep boreholes performed using a wireline formation tester (WFT) tool. We carried out PMT measurements as part of the characterization and appraisal of potential sites for a deep geological repository for radioactive waste in Switzerland. We performed multiple PMT inflation cycles to infer in situ static shear moduli at six stations spread across four boreholes. PMT-derived static shear moduli results were consistent with static shear moduli derived from sonic logs using independent dynamic-to-static elastic moduli transformations. PMT-derived static shear moduli and laboratory-derived static elastic moduli using samples from coring performed at the depths of the PMT stations were consistent, with slightly lower laboratory values. Furthermore, we report dynamic-to-static shear moduli transformations by using laboratory-scale data obtained on cores and field-scale derived from sonic logs and PMT. We observed differences between static and dynamic shear moduli derived from laboratory scale using cores and field scale using sonic logs and PMT. We report linear trend slopes of about 0.5 for the laboratory data and 0.7 for the field data. These first results show the viability of in situ PMT in deep boreholes with a WFT tool, as it can be performed at multiple depths in a single run, in a time-efficient manner, and in combination with micro-hydraulic and sleeve fracturing stress tests for an integral approach to in situ geomechanical assessment.

Random Forest and Decision Tree Facies Classification Models for Well Log Data of the Mishrif Formation from Basrah Oil Company, Southern Iraq

Facies collected from wells drilled in the study area were interpreted manually by using cores at every 10 meters of depth during well drilling. This depth of cores does not give true facies of all wells because the cores every 10 meters are considered very large. Extracting cores is financially expensive and takes a long time. The methodology of machine learning consists of four steps (Data gathering, Data preprocessing, Model training, and Model evaluation). This work intends to apply two of the supervised machine learning techniques random forest and decision tree models. (1) Data gathering, this dataset was collected from the Basrah Oil Company. It contains of ten wells (B-3, B-4, B-5, B-15, B-17, B-18, B-19, B-34, B-39, and B-40). Every well contains six features (logs): Sonic log, Resistivity Deep, Micro Spherically Focused Log, Neutron porosity, Density log, and Gamma-ray. Also, the dataset contains ten facies labels: mudstone, wack stone, packstone, roundstone, floatstone, shale, mud and wack stone, wack and pack stone, pack and grain stone and pack and float stone. These logs cover all the thickness of the Mishrif Formation, which is the goal of our study. (2) Preprocess, the data must be cleaned of outlier values; these values reduce the accuracy of the model during training. It is necessary at this stage to understand the relationships between all the features, because the highly correlated relationship between any two features, the more useless it will be in machine learning. The well B-5 blinded it for training to demonstrate the ability of machine learning models to predict lithofacies, then splitting randomly the dataset into 70% for training, validation 10%, and 20% for testing to verify the performance. (3) Training two models machine learning models decision tree and random forest. (4) Four statistics are computed for two models from the confusion matrix (accuracy of classification, recall, precision, and F1-score) showing the random forest was more accurate than the decision tree because the random forest model deals very well with this amount of dataset. Receiver Operating Characteristics curves of the random forest model have obtained the largest Area Under the Curve than the decision tree, is positive and above the main diagonal for all lithotypes, and the values for all classes reached more than 95%, except class 6 (shale), because of the 100% accuracy of classification. Facies classification by machine learning approach has two benefits (1) Increased accuracy of describing oil reservoirs and (2) Reducing the time, which a geologist needs to interpret logs data.

Exploring K-Means Clustering Efficiency: Accuracy and Computational Time across Multiple Datasets

Exploring K-Means Clustering Efficiency: Accuracy and Computational Time across Multiple Datasets

Subsurface Imaging in Urban Areas With Ambient Noise Using DAS and Seismometer Data Sets: Granada, Spain

AbstractDistributed acoustic sensing (DAS) is an innovative technology with great potential for acquiring seismic data sets in urban areas. In this work, we check the suitability of a DAS data set acquired in Granada (Spain) for retrieving subsurface reflectivity from ambient noise. The fiber‐optic is a pre‐existing underground telecommunication cable that crosses the city from Northwest to Southeast. We use a 10 hr recording of strain rate from a 2020 experiment to obtain seismic reflections using the autocorrelation method. We compare the DAS results with reflections obtained from seismic ambient noise recorded in nine seismometers deployed close to the fiber‐cable for 7 days in November 2022. The novel approach proposed in this study for the identification of the reflections is to use autocorrelations after bandpass filtering for specific central frequencies and to check the stability of the signals over a broad frequency band. Microtremor Horizontal to Vertical Spectral Ratio (MHVSR) measurements at a total of 14 stations, five of them outside the city, help to constrain the reflection interpretation. These include one station at the borehole that reaches the basement in the Granada Basin crossing all the Cenozoic units. We use the legacy sonic log to obtain a relationship between frequencies of MHVSR peaks and depth. Autocorrelation and MHVSR methods give consistent results delineating bedrock depth deeper than 1,000 m in Granada. These results confirm that DAS can provide valuable subsurface information in urban areas.

An Integrated Machine Learning Workflow to Estimate In Situ Stresses Based on Downhole Sonic Logs and Laboratory Triaxial Ultrasonic Velocity Data

AbstractThe optimum performance of various subsurface operations such as stimulation treatments, wellbore drilling, horizontal well placement, underground mining, and tunneling rely on accurate estimation of the in situ stresses. This study presents an integrated machine learning (ML) workflow for the reliable determination of the in situ stress in subsurface rock formations. The study workflow was completed in three phases. In the first phase, six supervised ML regression techniques were employed to develop the stress prediction models using laboratory true triaxial ultrasonic velocity tests (labTUV) data of subsurface rock samples retrieved from well 16A(78)‐32 located at the Utah FORGE geothermal site. In the second phase, subsurface geological formations were classified into rock facies using an unsupervised K‐means clustering algorithm. Finally, in the third phase, the optimized ML models were employed for predicting the in situ stresses in the corresponding rock facies in the well 16A(78)‐32 using field sonic logs. A comparison of evaluation metrics revealed the superior performance of ANN models for horizontal and vertical stress predictions with root mean squared error of 1.5, 0.6, and 1.7 MPa, and determination coefficient (R2) of 0.98, 0.98, and 0.96, for the testing/validation phases, respectively. The generalization capability of ML models was explored by uncovering the underlying physics. The mathematical expressions of constitutive relations were extracted from three ANN models. Further, a total of five rock facies were identified in the subsurface geological formations. The novel workflow would be capable of delivering reliable in situ stress profiles in subsurface geological formations without performing expensive field tests.

Field test of internal visualization of deep-water pile foundation using ultrasonic waves

Early-age internal visualization is critical for timely assessing the quality of pile foundations, especially as super cross-sea bridges increase the diameter and length of pile foundations beyond the capabilities of traditional cross-hole sonic logging (CSL). This article introduces two innovations. First, ultrasonic instruments are specially developed to transmit waves through a concrete pile with a diameter of 4 m, a length of 92 m, and a water depth of 63 m, before the full hardening of concrete. Second, an imaging method is proposed to upgrade traditional CSL from 1D detection to 3D visualization. Both large-scale experiments and field tests are tested in the first 7 days after the concrete casting. The experiments successfully visualize typical construction defects, achieving a detection resolution of 18 cm for layered defects and 15 cm for void defects. During the field test, a low-pixel region is observed at the pile head, extending approximately 2.1 m deep, attributed to mud floating up during concrete casting into the steel casing. These developments in instrument technology and visualization methods provide essential tools for the quality evaluation of super pile foundations. Furthermore, the visualization data can support digital twin modeling in subsequent stages of construction and maintenance planning.

A comprehensive crosshole seismic experiment in glacial sediments at the ICDP DOVE site in the Tannwald Basin

Abstract. Glaciers have shaped the Alpine landscape by carving deep valleys and depositing sediments to form overdeepened basins. Understanding these processes provides information on the evolution of the climate and landscape. One such overdeepened structure is the Tannwald Basin (ICDP site 5068_1) north of Lake Constance, which was formed by the Rhine Glacier in several glacial cycles. In order to study these sediments and their seismic properties down to about 160 m depth, we conducted seismic crosshole experiments between three boreholes, obtaining compressional (P) wave data. The P-wave data are generated by a sparker source and recorded by a 24-station hydrophone string. We present the data acquisition and review our approach for future optimization, suggesting a finer time sampling interval and a separate registration of borehole and surface receivers. Travel-time tomography of the P-wave first-arrival picks under geostatistical constraints yields initial subsurface models. The tomograms correlate well with cased-hole sonic logs and the lithology derived from the core of one of the boreholes. These results will be further investigated in future research, which will include full-waveform inversion (FWI) to obtain high-resolution subsurface models.

Improvement of Sonic Logging Porosity Calculation Method in South Sulige Block

Abstract South Sulige Block is a lithologic gas reservoir with low porosity and permeability, low pressure and low abundance, of which the heterogeneity is very high and pore structure is complex. Due to the influence of lithology and pore structure, the skeleton value is uncertain and it is hard to give a fixed value. It is difficult to obtain the true porosity of formation accurately by using the traditional method of calculating porosity by Wyllie formula in sonic logging. In this paper, according to the principle of sonic logging and rock response characteristics, combined with core analysis data of this area and adjacent areas, using the acoustic time and core analysis porosity regression formula to calculate porosity first, and then, according to different shale content, the corresponding shale correction is carried out for heavy shale content intervals on the basis of core regression formula, which eliminates the porosity calculation error caused by the influence of shale content. Practical application shows that the porosity calculated by this method is in good agreement with the results of core analysis, and can reflect the reservoir characteristics and production capacity of the formation more truly.

Using the Conventional Method to Estimate Water Saturation of Khasib Formation in East Baghdad Oil Field Central Area

The water saturation level may change due to geological changes or human intervention, like earthquakes, excavation, injection, or extraction of fluids from underground reservoirs. This work helps the reader understand how to use electrical logging to acquire a suitable water saturation value in resistivity and dielectric studies. In the East Baghdad field, two wells that were drilled into the Khasib Formation had their petrophysical parameters assessed using IP software. This allowed for the determination of water saturation using the available well logs, including neutron, density, SP, sonic, gamma ray, and resistivity logs for wells EB-25, and EB-29. The limestone section in the Khasib formation represents calcite, the main mineral found. We counted the amount of shale in each well using gamma-ray logs. The porosity at the reservoir unit was determined using a sonic log. Archie's equation and resistivity data are also used to compute fluid saturation. Taking m=n=2, a=1, x=1.76, and Rw=0.033 for Khasib formation. The link between Archie's and Raiga's coefficients, a, m, n, and x, is primarily the basis for this discussion. There is high agreement when this equation is used. In clean formations (non-shaly). this formula can be regarded as a reliable indicator of water saturation in situations where porosity techniques other than acoustic logs are unavailable. It's important to note that this equation provides accurate and realistic answers for rocks with transit times between 40 and 150 As/ft. Porosity knowledge is not required for such an assessment.

Pattern recognition approach (PRA) for identifying oil reservoir lithology of Camaal oil field, Yemen

The accurate determination of reservoir lithology remains a challenge in petroleum engineering. There are some conventional techniques available to determine the lithology. However, the application of those techniques has been long and complex. So, the main goal of this study is to simplify the identification of reservoir lithology. This paper presents a Pattern Recognition Approach (PRA) to identify the reservoir lithology simply and accurately. It is type of artificial neural network. Four wells from the Camaal Field were chosen to develop this approach. Around 32400 data points from the previous wells were digitized. The PRA approach used depth, gamma ray, lithology, sonic, neutron, and density logs as inputs. The model classifies lithology into permeable and impermeable rocks, further categorizing them into clastic and carbonate rocks, and subsequently into specific types into sand, sandstone, dolomite and limestone. The results show that the proposed approach provides a suitable prediction of lithology with higher accuracy compared with actual lithology. The model demonstrates high accuracy rates in identifying various lithologies, with overall accuracies of 76.2% for permeable/impermeable rocks, 94.9 for clastic/carbonate rocks, 86.2% for sand/sandstone, and 92.8% for dolomite/limestone.

Three-Dimensional Coupled Temporal Geomechanical Model for Fault-Reactivation and Surface-Deformation Evaluation during Reservoir Depletion and CO2 Sequestration, Securing Long-Term Reservoir Sustainability

Assessing reservoir subsidence due to depletion involves understanding the geological and geophysical processes that lead to ground subsidence as a result of reservoir fluid extraction. Subsidence is a gradual sinking or settling of the Earth’s surface, and it can occur when hydrocarbons are extracted from underground reservoirs. In this study, a time-integrated 3D coupled geomechanical modeling incorporating the fourth dimension—time—into traditional 3D geomechanical models has been constructed utilizing seismic inversion volumes and a one-dimensional mechanical Earth model (1D MEM). The 3D geomechanical model was calibrated to the 1D MEM results. Geomechanical rock properties were derived from the density and sonic log data that was distributed with conditioning to the seismic inversion volumes obtained from running pre-stack inversion. The standard elastic parameter equations were used to generate estimates of the elastic moduli. These properties are dynamic but have been converted to static values using additional equations used in the 1D MEM study. This included estimating the Unconfined Compressive Strength. In situ stresses were matched using different minimum horizontal principal stress gradients and horizontal principal stress ratios. The match is good except where the weak carbonate faults are close to the wells, where the Shmin magnitudes tend to decrease. The SHmax orientations were assessed from image log data and indicated to be 110° in the reservoir section. A time-integrated 3D coupled simulation was created using the finite-element method (FEM). The effective stresses increase while there is depletion in all directions, especially in the Z direction. The predicted compaction in the reservoir and overburden was 350 mm. Most of the compaction occurs at the reservoir level and dissipates towards the surface (seabed). Furthermore, the case displayed no shear failure that might cause or fault reactivation in the reservoir interval (Kangan–Dalan Formations) located in the simulated area. In this study, we applied an integrated and comprehensive geomechanical approach to evaluate subsidence, fault reactivation and stress alteration, while reservoir depletion was assessed using seismic inversion, well logs, and experiment data. The deformation monitoring of geological reservoirs, whether for gas storage or hazardous gas disposal, is essential due to the economic value of the stored assets and the hazardous nature of the disposed materials. This monitoring is vital for ensuring the sustainability of the reservoir by maintaining operational success and detecting integrity issues.

Petrophysical identification and characterization of Cenozoic hydrocarbon reservoir zones at the R field, northern Rovuma Basin, Tanzania

ABSTRACT Cenozoic reservoirs of northern Rovuma Basin, Tanzania, have not yet been characterized in detail. Combined well log patterns and fracture indicators have not been presented before. This work used petrophysical analysis of density, neutron, gamma ray (GR), resistivity and sonic well logs to characterize reservoir zones at the study area. Twelve reservoir zones (A–L) have been identified based on lithological properties and resistivity anomalies. The presence of hydrocarbons in these zones is concluded based on acoustic parameters involving acoustic impedance and V p / V s ratio, and ∆TC and neutron porosity crossplots. Fractured sedimentary fills in different stratigraphic levels were assessed based on resistivity-logs crossplots. Results show that eleven zones (A–F and H–L) are gas-filled and zone G is devoid of hydrocarbon. We have shown that, in areas with resistivity anomaly, all GR-resistivity combined patterns may be used to indicate hydrocarbon bearing layers except when there is a co-occurrence of funnel-shaped pattern for both logs. Distribution of fractures and hydrocarbons suggest the existence of compartmentalized reservoirs. These reservoirs have been discussed based on a geoseismic model created to support our arguments. Our technique has allowed easier identification of hydrocarbon bearing zones through well Rx. This technique may be applied in other sedimentary basins.

Sonic P-wave attenuation based on fluid pressure differential between joint and matrix

When P-wave compresses in-situ rocks saturated with fluid, there is a fluid pressure differential between compliant joints and stiff matrix. The pressure differential automatically drives a mesoscale squirt between joint wall and matrix interior. This mechanism is likely to dominate sonic P-wave attenuation in the field. A new model of P-wave attenuation based on the mesoscale squirt is established, in which the first porosity is represented by matrix pores while the second porosity is represented by joint. Sonic log of a sandstone reservoir in Saudi Arabia is used to illustrate the model. Both estimated phase velocity and measured quality factor of sonic P-wave are accurately simulated. The results show that for the sandstone reservoir, the relative second porosity is 5%, inter-joint distance is 0.1 m, squirt permeability at very low frequencies is about half of Darcy permeability of the matrix, and joint gap is 800 μm (quadruple grain diameter of 200 μm). Surprisingly, the model of joint-matrix squirt is also capable of reproducing seismic P-wave attenuation measured in the shallow crust of southern California. Therefore, mesoscale squirt across the joint wall is probably the underlying mechanism of attenuation for sonic and seismic P-waves shallower than 10 km.

A Model-Based Systematic Innovative Design for Sonic Logging Instruments in Natural Gas Wells.

With the continued development of natural gas extraction technologies, the accurate determination of downhole temperature and pressure has become increasingly important. It is crucial for the optimization of gas well production and an important measure to prevent accidents. However, existing logging instruments have a series of deficiencies in measurement and cannot adequately monitor modern natural gas. In response to these problems, in this paper, we propose a new model-based systematic innovation design method for designing logging instruments and simulations using finite element software. Our research results confirm the theoretical and practical utility of this model-based design method and provide a novel approach to designing logging instruments.

Drilling data-calibrated shale compaction models for pore pressure evaluation from geophysical well logs in the North Alpine Foreland Basin, SE Germany

This paper combines quality-controlled pore pressure measurements and indicators with geophysical well logs from 310 deep wells to evaluate the pore pressure distribution in the overpressured North Alpine Foreland Basin in SE Germany. Previous studies relied chiefly on low-resolution check shots and vertical seismic profiles calibrated to only a few pore pressure measurements. The current study, based on sonic and electrical resistivity logs, indicates that more than one shale normal compaction trend is required to appropriately assess pore pressure from geophysical well logs. Thereby, the necessary adjustments of the derived compaction trends apply to both sonic and resistivity data, which possibly reflect a gradual change in the mineralogical composition with decreasing clay content or progressing cementation from north to south or an increase in horizontal loading-driven compaction towards the North Alpine Thrust Front. The newly derived compaction-based pore pressure distribution is combined with drilling data-based pore pressure evaluations and offers a comprehensive update of previous pore pressure investigations in the North Alpine Foreland Basin. Our study is therefore of key importance for optimized well planning and cost-effective drilling in the course of the recent expansion of deep geothermal energy exploration in the overpressured North Alpine Foreland Basin.

Estimation of Porosity for Sedimentary Rocks and Volcanic Rocks from Sonic Log Data in the Futagawa Fault Drilling

Estimation of Porosity for Sedimentary Rocks and Volcanic Rocks from Sonic Log Data in the Futagawa Fault Drilling

Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves

Porosity assessment is a vital component for reservoir evaluation in the oil and gas sector, and with technological advancement, reliance on conventional methods has decreased. In this regard, this research aims to reduce reliance on well logging, purposing successive machine learning (ML) techniques for precise porosity measurement. So, this research examines the prediction of the porosity curves in the Sui main and Sui upper limestone reservoir, utilizing ML approaches such as an artificial neural networks (ANN) and fuzzy logic (FL). Thus, the input dataset of this research includes gamma ray (GR), neutron porosity (NPHI), density (RHOB), and sonic (DT) logs amongst five drilled wells located in the Qadirpur gas field. The ANN model was trained using the backpropagation algorithm. For the FL model, ten bins were utilized, and Gaussian-shaped membership functions were chosen for ideal correspondence with the geophysical log dataset. The closeness of fit (C-fit) values for the ANN ranged from 91% to 98%, while the FL model exhibited variability from 90% to 95% throughout the wells. In addition, a similar dataset was used to evaluate multiple linear regression (MLR) for comparative analysis. The ANN and FL models achieved robust performance as compared to MLR, with R2 values of 0.955 (FL) and 0.988 (ANN) compared to 0.94 (MLR). The outcomes indicate that FL and ANN exceed MLR in predicting the porosity curve. Moreover, the significant R2 values and lowest root mean square error (RMSE) values support the potency of these advanced approaches. This research emphasizes the authenticity of FL and ANN in predicting the porosity curve. Thus, these techniques not only enhance natural resource exploitation within the region but also hold broader potential for worldwide applications in reservoir assessment.

Implication of fast shear wave azimuth and critically stressed fractures in a coalbed methane reservoir

The orientation of the fractures and stresses within coal seams plays a critical role in gas production from coalbed methane reservoirs. In this study, we used resistivity images and sonic logs to investigate these parameters, aiming to (1) establish the relationship between fracture orientations and the polarization angles of fast shear waves and (2) detect active fractures in the coal seam. Shear waves in anisotropic formations are split into fast and slow components, with Alford’s rotation method used to determine the polarization angles of the fast shear wave. We found that the fast shear wave aligns with the direction of higher fracture intensity. Subsequently, we incorporated the poroelastic strain model to estimate the vertical ([Formula: see text]), maximum horizontal ([Formula: see text]), and minimum horizontal ([Formula: see text]) stresses in the wellbore. These stress magnitudes aided in identifying the faulting regime and were corroborated by vertical opening mode fractures. Validation of [Formula: see text] and [Formula: see text] involved comparisons with the breakout width in image logs and the closure pressure observed during hydraulic fracturing treatments. Applying Mohr’s Coulomb criteria, the stress model discerned the state of the fractures, transforming the stress magnitudes into shear and effective normal stress on each fracture plane. Our observations indicated that the identified fractures existed in a noncritically stressed condition, suggesting a lack of interconnectivity among them. These findings correspond to the absence of gas production to date, providing insights into the dynamics of fractures and their impact on production behavior.

Comparative analysis of bulk and velocity-derived density data in deep wells in the Adriatic Region (Italy)

This work is based on a systematic comparison between two datasets related to density data of geological formations crossed in 13 deep wells. The wells are located in the Adriatic area and reach depths of over 5000 meters. The main lithologies involved include sandstones, marls, clays, evaporite, and carbonate rocks. The first dataset concerns density values obtained from the analysis of sonic logs recorded along the wells, by applying the Gardner relation. The second dataset, on the other hand, refers to actually acquired density log measurements. Differences among the values in the two datasets have been calculated. The aim behind this work is to assess the reliability of rock densities estimated using the Gardner equation by comparing them to measurements obtained through density logs, despite many factors influencing the log density. This comparison of the densities obtained from various lithologies and geological formations leads us to draw, in this region, some initial considerations regarding the applicability and accuracy of Gardner formula, usually considered as standard reference, since the density log is generally available only within the reservoir. In the area we analyzed it is observed that the density values estimated from sonic velocities are underestimated for the Plio-Quaternary formations characterized by clayey-sandy lithologies by at least 0.1 g/cm3; whereas, densities of the carbonate sequences are overestimated by the same extent. Noteworthy, the density estimates deviate from the real values especially for gypsum units, overestimating by a factor of approximately 0.3 g/cm3. The results we obtained emphasize differences in density values when using the Gardner formula and suggest the need for taking into account the possible errors in the specific geological context or instead lithologies, such as those explored in this study.