Properties Of Rock Formations Research Articles

FOUNDATION AND ASSESSMENT OF OPTIMAL FRACTURE GEOMETRY IN MULTI-STAGE HYDRAULIC FRACTURING OF HORIZONTAL WELLS

The article is dedicated to the calculation and assessment of the optimal geometry of fractures in the context of conducting hydraulic fracturing in horizontal wells. The significance of correctly determining the geometry of fractures to maximize well productivity and the efficiency of the hydraulic fracturing process is considered. The paper analyzes various methods for calculating and evaluating the optimal geometry of fractures, including numerical modeling, experimental research, and practical observations.Additionally, the article covers the results of experimental studies aimed at analyzing the behavior of fractures in different geological conditions. These studies include the analysis of physical and mechanical properties of rock formations, as well as the influence of well parameters on fracture formation. The obtained results can be useful for engineers and specialists in the oil and gas industry in designing and conducting hydraulic fracturing operations to increase hydrocarbon recovery from reservoirs.

Identification of Lithology from Well Log Data Using Machine Learning

INTRODUCTION: Reservoir characterisation and geomechanical modelling benefit significantly from diverse machine learning techniques, addressing complexities inherent in subsurface information. Accurate lithology identification is pivotal, furnishing crucial insights into subsurface geological formations. Lithology is pivotal in appraising hydrocarbon accumulation potential and optimising drilling strategies.
 OBJECTIVES: This study employs multiple machine learning models to discern lithology from the well-log data of the Volve Field.
 METHODS: The well log data of the Volve field comprises of 10,220 data points with diverse features influencing the target variable, lithology. The dataset encompasses four primary lithologies—sandstone, limestone, marl, and claystone—constituting a complex subsurface stratum. Lithology identification is framed as a classification problem, and four distinct ML algorithms are deployed to train and assess the models, partitioning the dataset into a 7:3 ratio for training and testing, respectively.
 RESULTS: The resulting confusion matrix indicates a close alignment between predicted and true labels. While all algorithms exhibit favourable performance, the decision tree algorithm demonstrates the highest efficacy, yielding an exceptional overall accuracy of 0.98.
 CONCLUSION: Notably, this model's training spans diverse wells within the same basin, showcasing its capability to predict lithology within intricate strata. Additionally, its robustness positions it as a potential tool for identifying other properties of rock formations.

Wastewater leakage identification in WWTP and concrete pipelines using integrated geophysical imaging methods

Industry and mining activities covers a broad spectrum of processes that dispose an abundant amount of wastewater. Given the vast array of industries, there are equally large number of heavy metals, organic solvents, and hydrocarbons pollutants associated with its wastewater, posing a great threat to aquifers, especially groundwater. Main contributor of this polluting scheme are from the leakage of underground/surface storage tanks, effluent pipework, industrial sewers, and waste injection system. To ascertain the presence of pollution in the subsoil and groundwater resources, an integrated geophysical method can be the alternative of non-invasive methods. The presence of contaminants in subsoil alters the physical properties of rock formations and decrease pore-water resistance, therefore it can be mapped by geoelectrical resistivity. In this paper, study case was conducted in Medan Industrial Estate, Indonesia, to identify potential leakage under the Wastewater Treatment Plant (WWTP) and its pipework, using Geoelectrical Wenner and Magnetotelluric Method with 5 lines in the interval of 40-60 meters and Magnetotelluric Method throughout the 175 meters of pipework in the production area. Based on the Geoelectrical Method, an anomaly with a low resistivity value was detected within 7 meters of the ground depth. Meanwhile, in the Magnetotelluric Method, a moderate permittivity value was detected that is local and located within 2 meters depth which is where the pipe and the bottom of the WWTP storage tank is located. In conclusion, based on the results obtained from the Geoelectrical and Magnetotelluric Methods that have been carried out in the WWTP area and along the wastewater pipeline leading to the WWTP, no anomalies were found that lead to waste water leakage.

Accurate Petrophysical Interpretation of Carbonate using the Elemental Capture Spectroscopy (ECS)

Elemental capture spectroscopy (ECS) is an important tool in the petroleum industry for determining the composition and properties of rock formations in a reservoir. Knowledge of the types and abundance of different minerals in the reservoir is crucial for accurate petrophysical interpretation, reservoir engineering practices, and stratigraphic correlation. ECS measures the elemental content of the rock, which directly impacts several physical properties that are essential for reservoir characterization, such as porosity, fluid saturation, permeability, and matrix density. The ability to accurately determine these properties leads to better reservoir mapping, improved production, and more effective resource management. Accurately determining the mineralogy and porosity of carbonate rocks and other materials is the aim of this paper. Calcite, dolomite, quartz, clay (illite), anhydrite, and pyrite, in addition to water as a fluid, are taken into account in the computation. The formation's lithology and porosity can be ascertained from this data. When compared to the core descriptions in the geological report, the results demonstrated a distinct zone of unique lithology with good prediction accuracy.

Integrating drilling parameters and machine learning tools to improve real-time porosity prediction of multi-zone reservoirs. Case study: Rhourd Chegga oilfield, Algeria

Porosity is a key variable for hydrocarbon reservoirs evaluation. It can be directly determined in laboratory tests using core samples or calculated indirectly from well logs. However, these methods are expensive and time consuming affecting the cost of supply of oil produced. Monitoring drilling variables in real time as a borehole is drilled can provide a cheap, initial insight to various rock formation properties. Investigating the relationships between rock porosity and drilling variables is therefore important. This study applies machines learning (ML) relate rock porosity to a range of measured drilling variables in several types of reservoir. Four ML models are evaluated: K-nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and eXtreme Gradient Boosting (XGBoost). A measured drilling and porosity dataset from the Rhourd Chegga oilfield (SE Algeria) is compiled from its multi-zone reservoir system. The drilling variables from nine vertical wellbores considered are: weight on bit, torque, standpipe pressure, drill string rotation speed, rate of penetration and its inverse, pump rate and bit depth. The recorded data is derived from three distinct reservoir zones: Trias (T1), Rhourd Chegga sandstone (RDC), and the Quartzite Hamra tight reservoir (QH). Porosity predictions were derived in two ways: (1) for each reservoir separately; (2) for the three reservoirs combined. Prediction error analysis was conducted in terms of the coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE). The XGBoost model yielded the best porosity predictions for the individual and combined reservoirs. Prediction errors generated for reservoir T1 were (R2 ≈ 0.865, RMSE≈0.866, MAE≈0.692); for reservoir RDC were R2 ≈ 0.935, RMSE≈0.848, and MAE≈0.772; and, for reservoir QH were R2 ≈ 0.743, RMSE≈0.794, and MAE≈0.573. For the combined (multi-zone) reservoir (T1+RDC + QH), porosity predictions were less accurate, and the XGBoost model generated prediction errors of R2 ≈ 0.76, RMSE≈1.259, and MAE≈1.027.

Main types of reservoirs of pre-jurassic deposits in the Ustyurt region

This article compiles and presents the tangible records encompassing drilling operations, testing procedures, and geophysical loggings conducted in the Ustyurt oil and gas region of Uzbekistan. These consolidated materials provide a comprehensive overview of the exploration efforts undertaken in this region. The dataset includes information on core samples extracted from drilling, their subsequent analysis, and the outcomes of tests conducted on intervals within the Pre-Jurassic deposits. Through a meticulous examination of core samples, drilled wells, and geophysical loggings, the study delves into the reservoir properties of rock formations in this geological region. The research identifies distinct groups of rocks within the Pre-Jurassic complex, categorized by their porosity characteristics. This categorization serves as a foundational step in understanding the geological composition and potential reservoir properties of these formations. An important outcome of this analysis is the identification of five distinct types of reservoirs based on the structure of the pore space within the pre-Jurassic sediments. This categorization holds immense potential for future predictions and assessments concerning these sedimentary deposits. By amalgamating core data, geophysical readings, and testing outcomes, the research contributes to a holistic understanding of the geological attributes and reservoir potential of the Ustyurt oil and gas region. This foundational information sets the stage for informed decision-making in exploration, production, and resource management activities within this significant geological area.

Drilling Mud Influence on Sandstone Poroelastic Parameters

Abstract: The most critical challenge faced during drilling operations is related to the stability of the well. Additionally, drilling mud plays a crucial role in wellbore stability, as one of its main uses is to support the wellbore wall during the drilling operation. However, ignorance of the effects of drilling mud on the mechanical properties of rock formation can also lead to well failure. The stability of the wellbore is also influenced by the pore pressure during the drilling process. The analysis of changes in rock poroelastic parameters after drilling mud saturation was found to be a useful research regarding the above mentioned. Therefore, the measurement of the dynamic Young’s modulus, Poisson’s ratio and Biot’s coefficient of sandstone samples was carried out to determine their trends of variations with confining pressure in different conditions such as dry, water and drilling mud filtrate saturation. The findings indicate that both the dynamic Young modulus and the Poisson’s ratio of the sandstone rock increased after saturation with water and drilling mud filtrate, while the Biot’s coefficient was reduced. Furthermore, the velocity of the P wave, the dynamic Young modulus and the dynamic Poisson ratio of the sandstone rock were proportional to the confining pressure, while the Biot’s coefficient were inversely proportional to the confining pressure. The results imply that the effective stress calculation can be influenced by changes in poroelastic parameters established from geophysical measurements, and risk management of wellbore stability stability was increased.

Modeling and Simulation of Rock Bits Based on Mega Drilling Data

Rock bits directly undertake the task of fracturing rock under earth during drilling operation. Accurate prediction of Rate of penetration (ROP) play an important role in reducing drilling costs and shortening the drilling cycle. This paper introduces ROP prediction methods including empirical mathematical modeling, computer simulation of bit-rock interaction and BP neural network model based on mega drilling data. The most significant factors affecting ROP are summarized as follows: weight on bit(WOB), rotation speed, rock formation properties, bit cutting structure, etc., which have a complex multi-parameter nonlinear relationship with ROP. The drilling data features complexity and big volume. Therefore machine learning is a best way to model the relationship between main factors and ROP. The advantages and disadvantages of the different ROP modeling methods are analyzed and compared. It shows that the BP neural network model based on mega drilling data demonstrates high accuracy, and is the future development direction in ROP prediction.

Study on the acceleration of single‐cone PDC composite bit

The single‐cone bit has become the first choice for slim hole sidetracking and deep well drilling with its unique rock breaking method and high ROP (rate of penetration). The single‐cone bits currently used in the oil field all use cemented carbide inserts, with its main failure mode being of early excessive wear of the cutting teeth. In order to improve the adaptability of single‐cone bits to hard and highly abrasive formations, a single‐cone PDC composite bit is designed. According to the characteristics of the tooth profile, the way of tooth arrangement and the way of contact between the cutting teeth and the rock, the acceleration equation to the cutting teeth of the single‐cone PDC composite bit is established. The shaft inclination angle of the cone, the position and height of the PDC teeth, the radius of the PDC teeth, the lateral rotation angle, and the front inclination angle on the acceleration are studied. The acceleration of the single‐cone bit is verified by numerical simulation and drilling experiment of rock‐breaking. Rock formation properties have an impact on the acceleration of the cutting teeth, with the acceleration of the cutting teeth in hard rock formations being higher than that in soft rock formations. On the premise that the bearing life of the single‐cone bit is allowed, the value of the shaft inclination angle β can be approached to 70°. This research lays a theoretical foundation for the dynamic research and wear analysis of single‐cone PDC composite bit.

Deep reinforcement learning control of hydraulic fracturing

Hydraulic fracturing is a technique to extract oil and gas from shale formations, and obtaining a uniform proppant concentration along the fracture is key to its productivity. Recently, various model predictive control schemes have been proposed to achieve this objective. But such controllers require an accurate and computationally efficient model which is difficult to obtain given the complexity of the process and uncertainties in the rock formation properties. In this article, we design a model-free data-based reinforcement learning controller which learns an optimal control policy through interactions with the process. Deep reinforcement learning (DRL) controller is based on the Deep Deterministic Policy Gradient algorithm that combines Deep-Q-network with actor-critic framework. Additionally, we utilize dimensionality reduction and transfer learning to quicken the learning process. We show that the controller learns an optimal policy to obtain uniform proppant concentration despite the complex nature of the process while satisfying various input constraints.

Study on landslide susceptibility mapping based on rock\u2013soil characteristic factors

This study introduces four rock–soil characteristics factors, that is, Lithology, Rock Structure, Rock Infiltration, and Rock Weathering, which based on the properties of rock formations, to predict Landslide Susceptibility Mapping (LSM) in Three Gorges Reservoir Area from Zigui to Badong. Logistic regression, artificial neural network, support vector machine is used in LSM modeling. The study consists of three main steps. In the first step, these four factors are combined with the 11 basic factors to form different factor combinations. The second step randomly selects training (70% of the total) and validation (30%) datasets out of grid cells corresponding to landslide and non-landslide locations in the study area. The final step constructs the LSM models to obtain different landslide susceptibility index maps and landslide susceptibility zoning maps. The specific category precision, receiver operating characteristic curve, and 5 other statistical evaluation methods are used for quantitative evaluations. The evaluation results show that, in most cases, the result based on Rock Structure are better than the result obtained by traditional method based on Lithology, have the best performance. To further study the influence of rock–soil characteristic factors on the LSM, these four factors are divided into “Intrinsic attribute factors” and “External participation factors” in accordance with the participation of external factors, to generate the LSMs. The evaluation results show that the result based on Intrinsic attribute factors are better than the result based on External participation factors, indicating the significance of Intrinsic attribute factors in LSM. The method proposed in this study can effectively improve the scientificity, accuracy, and validity of LSM.

Prediction of Reservoir Parameters of Cambrian Sandstones Using Petrophysical Modelling—Geothermal Potential Study of Polish Mainland Part of the Baltic Basin

Important factors controlling the effective utilization of geothermal energy are favorable reservoir properties of rock formations, which determine both the availability and the transfer opportunities of reservoir fluids. Hence, crucial to the successful utilization of a given reservoir is the preliminary recognition of distribution of reservoir parameters as it enables the researchers to select the prospective areas for localization of future geothermal installations and to decide on their characters. The objectives of this paper are analyses and discussion of the properties of quartz sandstones buried down to a depth interval from about 3000 to under 5000 m below surface. These sandstones belong to Ediacaran–Lowery Cambrian Łeba, Kluki and Żarnowiec formations. The source data from the Słupsk IG-1 provided the basis for 1D reconstruction of burial depth and paleothermal conditions as well as enabled the authors to validate of the results of 2D models. Then, porosity distribution within the reservoir formation was determined using the modelings of both the mechanical and chemical compactions along the 70 km-long B’-B part of the A’-A cross-section Bornholm-Słupsk IG-1 well. The results confirmed the low porosities and permeabilities as well as high temperatures of the analyzed rock formations in the Słupsk IG-1 well area. Towards the coast of the Baltic Sea, the porosity increases to more than 5%, while the temperature decreases, but is still relatively high, at about 130 °C. This suggests the application of an enhanced geothermal system or hot dry rocks system as principal methods for using geothermal energy.

Advanced Methods of Joint Inversion of Multiphysics Data for Mineral Exploration

Different geophysical methods provide information about various physical properties of rock formations and mineralization. In many cases, this information is mutually complementary. At the same time, inversion of the data for a particular survey is subject to considerable uncertainty and ambiguity as to causative body geometry and intrinsic physical property contrast. One productive approach to reducing uncertainty is to jointly invert several types of data. Non-uniqueness can also be reduced by incorporating additional information derived from available geological and/or geophysical data in the survey area to reduce the searching space for the solution. This additional information can be incorporated in the form of a joint inversion of multiphysics data. This paper presents an overview of the main ideas and principles of novel methods of joint inversion, developed over the last decade, which do not require a priori knowledge about specific empirical or statistical relationships between the different model parameters and/or their attributes. These approaches are designated as follows: (1) Gramian constraints; (2) Gramian-based structural constraints; (3) localized Gramian constraints; and (4) joint focusing constraints. We provide a short description of the mathematical foundations of each of these approaches and discuss the practical aspects of their applications in mineral exploration.

Effective-Stress Coefficients of Porous Rocks Involving Shocks and Loading/Unloading Hysteresis

SummaryA critical review, examination, and clarification of the various issues and problems concerning the definition and dependence of the effective-stress coefficients of porous-rock formations is presented. The effective-stress coefficients have different values for different rock properties because the physical mechanisms of rock deformation can affect the various rock properties differently. The alteration of petrophysical properties occurs by the onset of various rock-deformation/damaging processes, including pore collapsing and grain crushing, and affects the values of the effective-stress coefficients controlling the different petrophysical properties of rock formations. The slope discontinuity observed in the effective-stress coefficients of naturally or induced fractured-rock formations during loading/unloading, referred to as a shock effect, is essentially related to deformation of fractures at less than the critical effective stress and deformation of matrix at greater than the critical effective stress. The hysteresis observed in the effective-stress coefficients of heterogeneous porous rocks during loading/unloading is attributed to elastic deformation under the fully elastic predamage conditions, and/or irreversible pore-structure-alteration/deformation processes.A proper correlation of the Biot-Willis coefficient controlling the bulk volumetric strain is developed using the data available from various sources in a manner to meet the required endpoint-limit conditions of the Biot-Willis coefficient, ranging from zero to unity. The modified power-law equation presented in this paper yields a physically meaningful correlation because it successfully satisfies the low-end- and high-end-limit values of the Biot-Willis coefficient and also provides a better quality match of the available experimental data than the semilogarithmic equation and the popular basic power-law equation. It is shown that the semilogarithmic correlation cannot predict the values of the Biot coefficient beyond the range of the data because it generates unrealistic values approaching the negative infinity for the Biot coefficient for the low-permeability/porosity ratio and unrealistically high values approaching the positive infinity for the high-permeability/porosity ratio. The basic power-law equation is not adequate either because it can only satisfy the low-end value but cannot satisfy the high-end value of the Biot coefficient. The correlation developed in this paper from the modified power-law equation is effectively applicable over the full range of the Biot-Willis coefficient, extending from zero to unity. To the best of the author's knowledge, this paper is the first to present an effective theory and formulation of the convenient correlation of the Biot-Willis poroelastic coefficient that not only satisfies both of the two endpoint-limit values of the Biot-Willis coefficient but also produces the best match of the available experimental data.

New approaches to the assessment of stability of rock rock arrays

When summarizing the results obtained on the stability of rock masses a discrepancy was found between the parameters of the strength properties of rock formations, determined according to existing methods and state standards, to their values under natural conditions. As a result of the studies, the degree of geomechanical knowledge of the rock mass of the Gaisky underground mine is significantly increased. Based on the numerical simulation of the stress-strain state of the ore and rock mass, the rationale for the optimal mining of reserves at a depth of -830 / -1390 m was substantiated. The main compressive stresses of the Gaisky deposit act in the sub-latitudinal direction along the axis of the chambers, creating stresses exceeding 100 MPa on their outcrops. If the compressive strength of the massif is up to 100 MPa, found by known methods, ore masses of the third stage (pillars) below the -910 m horizon should be destroyed. In assessing the stability of pillars (chamber walls) in the last 3 years, in some cases, disagreement arose. The calculated stresses exceed the maximum allowable ones, but the pillars (chamber walls) remain stable. It justifies the correction of the obtained values of the ultimate strength of rocks with correction factors and the introduction of the definition of reduced strength (227 MPa), in which the roof, walls of the chambers and pillars are in a stable state. As a result of the study, the obtained values of the stress state of the rock mass and its strength characteristics more realistically reflect the predicted destruction or stability of the rock mass at the mine. The performed studies contribute to a more justified adjustment of mining technology parameters while ensuring the safety of mining operations.

Structure, kinematics and composition of fluid-controlled brittle faults and veins in Lower Cretaceous claystones (Lower Saxony Basin, Northern Germany): Constraints from petrographic studies, microfabrics, stable isotopes and biomarker analyses

Investigation of fault rocks is crucial for the evaluation of sealing properties of rock formations considered as oil and gas storage or for the disposal of heat-generating radioactive waste. Even in tight rock formations, fluid flow may concentrate along brittle faults. The present study focuses on the sealing properties of faulted Lower Cretaceous clay- and siltstones encountered in drill cores from the central and eastern Lower Saxony Basin (Germany). Our investigations are based on a multidisciplinary approach including microstructural, mineralogical-geochemical, stable isotope and biomarker analyses.The new data suggest that preexisting discontinuities and/or sedimentary heterogeneities played a significant role for the increase in fluid pressure and formation of mode I veins within low permeability claystones. High-strain domains of fault cores consist of fine-grained fault gouge. A scaly fabric of varying deformation intensity is present around larger faults. Calcite-precipitation in faults and complex mineralized veins, and the differences in trace element concentrations of various veins, reflect several phases of palaeofluid activity. Calcite precipitation in the fault rocks led to elevated amounts of Mn, Ba and/or Sr. Isotope signatures indicate a local carbonate source for the origin of thin calcite coatings of fault planes, but a more complex palaeofluid interaction for the larger faults and for complex veins, consistent with the microfabrics. Biomarker signatures reveal the presence of mature hydrocarbons pointing to hydrocarbon migration in sections where steep, only weakly mineralized faults occur.The new results suggest that faulted claystones represent critical zones with a potentially reduced barrier capability consistent with published data.

Discerning the Contribution of Morphology and Chemistry in Wettability Studies

The wetting behavior of homogeneous systems is now well understood at the macroscopic scale. However, this understanding offers little predictive power regarding wettability when mesoscopic chemical and morphological heterogeneities come into play. The fundamental interest in the effect of heterogeneity on wettability is derived from its high technological relevance in several industries, including the petroleum industry where wettability is recognized as a key determinant of the overall efficiency of the water-flooding-based enhanced oil recovery process. Here, we demonstrate the use of the atomic force microscopy force curve measurements to distinguish the roles of chemistry and morphology in the wetting properties of rock formations, thus providing a clear interpretation and deeper insight into the wetting behavior of heterogeneous formations. Density functional theory calculations further prove the versatility of our approach by establishing benchmarks on ideal surfaces that differ in chemistry and morphology in a predefined manner.

Near Real-Time Classification of Iron Ore Lithology by Applying Fuzzy Inference Systems to Petrophysical Downhole Data

Fluctuating commodity prices have repeatedly put the mining industry under pressure to increase productiveness and efficiency of their operations. Current procedures often rely heavily on manual analysis and interpretation although new technologies and analytical procedures are available to automate workflows. Grade control is one such issue where the laboratory assay turn-around times cannot beat the shovel. We propose that for iron ore deposits in the Pilbara geophysical downhole logging may provide the necessary and sufficient information about rock formation properties, circumventing any need for real-time elemental analysis entirely. This study provides an example where petrophysical downhole data is automatically classified using a neuro-adaptive learning algorithm to differentiate between different rock types of iron ore deposits and for grade estimation. We exploit a rarely used ability in a spectral gamma-gamma density tool to gather both density and iron content with a single geophysical measurement. This inaccurate data is then put into a neural fuzzy inference system to classify the rock into different grades and waste lithologies, with success rates nearly equal to those from laboratory geochemistry. The steps outlined in this study may be used to produce a workflow for current logging tools and future logging-while-drilling technologies for real-time iron ore grade estimation and lithological classification.

Experimental Study of Electrical Resistivity to Rock Fracture Intensity and Aperture Size

Fracture geometric characteristics (FGC) such as fracture intensity, fracture network connectivity and aperture distribution are crucial features controlling the hydraulic and geotechnical properties of rock formations. Geophysical methods have been used to detect contrast in subsurface material. The Electrical Resistivity (ER) method is one of such methods. The method was applied in an experimental test to investigate ER response with varying fracture intensity If and aperture size Aper width in an experimental set-up at Kwame Nkrumah University of Science and Technology (KNUST, Kumasi, Ghana, geotechnical laboratory. The concepts of electrical resistivity variation with rock mass fracture intensity, and aperture were used to obtained experimental data. ER profiles, each measuring 0.9 m long were recorded using 4 electrodes deployed using the Dipole-dipole, Wenner and Schlumberger configurations from the experimental setup. To quantify the relationship between apparent resistivity and fracture intensity, scatter-plots were drawn with apparent resistivity as the abscissa and fracture intensity as well as aperture width as ordinate. There were strong positive linear and regressive correlations between ρa and fracture intensity. Mathematical relationships are established that relate the ER and fracture intensity, and ER with aperture width. The highest coefficient of determination R2 of 0.924 was represented for best fit equation, for relationship between apparent resistivity and fracture intensity. For aperture width, the best fit model was given a logarithmic relation as ρa =-465.46(Aperture width)2 +4880.5(Aperture width)+1295.5. The study demonstrates the potential usefulness of the ER approach in rock fracture characterisation investigations, which is economic, efficient and less time consuming compared to other methods, of subsurface fracture characterization such core drilling.

Inversion-based method to mitigate noise in borehole sonic logs

A major challenge in the interpretation of seismic measurements and sonic logs is the presence of deleterious noise that impacts the quality and reliability of the estimated seismic wavelets and seismic inversion products. Spatial averaging effects and borehole drilling damage can also bias the estimation of in situ stress and elastic properties from sonic logs. We have developed an inversion-based method to mitigate processing errors, spatial averaging effects, and borehole environmental noise on sonic logs, which does not require arbitrary numerical filters, effective-medium theory models, or time-consuming waveform reprocessing. The inversion-based method estimates layer-by-layer slownesses via joint inversion of shear and compressional logs measured in a vertical well, and it uses the estimated slownesses of the assumed horizontal layers to model noise-mitigated sonic logs. By making use of geometric and physical constraints for noise reduction implicit in the inversion-based method, we obtain sonic logs that more accurately reflect the physical properties of rock formations penetrated by wells. Sonic logs are efficiently modeled by invoking axial sensitivity functions. First, we test the inversion-based method with synthetic sonic logs contaminated with noise. Estimated layer-by-layer slownesses agree with those of the original model within a standard deviation of [Formula: see text], while effectively reducing the numerical noise included in the input measurements. When bed-boundary locations are unknown, we perform the inversion-based method by assuming bed boundaries uniformly spaced at the same sampling interval of sonic logs; in this case, although the accuracy of the estimated layer slownesses decreases, the noise on sonic logs decreases. Then, we apply the method to sonic logs acquired in the North Sea and estimate angle reflectivity from the noise-mitigated logs. Results verify the reliability of the inversion-based method to reduce biases in the calculated angle reflectivity within a few minutes of central processing unit time.