Well-logging Curves Research Articles

Prediction of Lithium Oilfield Brines Based on Seismic Data: A Case Study from L Area, Northeastern Sichuan Basin, China

Lithium is an important mineral resource and a critical element in the production of lithium batteries, which are currently in high demand. Oilfield brine has significant value as a raw material for lithium extraction. However, it is often considered a byproduct of oil and gas production and is either abandoned or reinjected underground. Exploration and development of oilfield brines can enhance the economic benefits of oilfields and avoid wasting resources. Current methods for predicting brine distribution rely on geological genetic analysis, which results in low accuracy and reliability. To address this issue, we propose a workflow for lithium brine prediction that uses seismic and logging data. We introduced waveform clustering control and used the mapping relationship between seismic waveforms and well-logging curves to predict high-quality reservoirs based on the electrical and physical properties of lithium brine reservoirs. In this workflow, the seismic waveforms were first clustered using singular value decomposition. The sample sets of well-logging properties were established for the target location. The target properties were divided into high- and low-frequency components and predicted separately. The predicted results of the high-quality reservoirs in the study area were verified using elemental content test results to demonstrate the effectiveness of the method. Our study indicates that well-logging property prediction constrained by waveform clustering can predict lithium brines in a carbonate reservoir.

Lithofacies identification from well-logging curves via integrating prior knowledge into deep learning

Lithofacies is a key parameter in reservoir characterization. With advances in machine learning, many researchers have attempted to predict lithofacies from well-log curves by using a machine-learning algorithm. However, existing models are built purely on data, which do not provide interpretability. In addition, lithofacies distribution is highly imbalanced. We incorporate domain knowledge into a gated recurrent unit network to force the model to learn from the data and knowledge. The domain knowledge that we use is expressed as first-order logic rules and is incorporated into the machine-learning pipeline through additional loss terms. Specifically, these rules are: (1) if the density is smaller than or equal to [Formula: see text], then the lithofacies is coal; (2) if the density is larger than or equal to [Formula: see text] or the neutron porosity is smaller than or equal to [Formula: see text], then the lithofacies is anhydrite; and (3) if the gamma-ray value is larger than or equal to [Formula: see text], then the lithofacies is shale. Here, [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] are the parameters that are learned by the model. By applying this domain knowledge, we aim to elucidate why the model predicts lithofacies as coal, anhydrite, or shale and reduce the effect of imbalanced data on the model’s performance. We evaluate the method on a data set from the North Sea, and the machine-learning pipeline with domain knowledge embedded is slightly superior compared with the baseline model that does not consider domain knowledge. One drawback of the method is that the domain knowledge that we provide only works for coal, anhydrite, and shale, which is incomplete. In future work, we will attempt to develop more rules that work for other types of lithofacies.

Nonstationary seismic–well tying with time-varying wavelets

Seismic–well tying is an important technique for correlating well-logging curves in depth with seismic traces in time. An appropriate seismic–well tying technique must account for two types of nonstationarity: the nonstationary time errors in the synthetic seismic trace caused by the inaccurate time–depth relationship established based on sonic-logging velocity and the nonstationary seismic signals due to the time-varying wavelets during wave propagation. The nonstationary problems related to the time–depth relationship and the time-varying wavelets are interrelated in seismic-well tying procedure. We implemented a nonstationary seismic–well tying method by iteratively updating the time-depth relationship and estimating the time-varying wavelets. From the estimated time-varying wavelets, we also estimated a Q-value by assuming that the subsurface medium has a constant Q at depth and used the constant Q to constrain the variation of the seismic wavelet during propagation. Then, we used the improved time–depth relationship and time-varying wavelets with the Q constraint for further iterations. In the iterative procedure, we quantified the accuracy of the seismic–well tying result using the correlation coefficient between the synthetic and the true seismic trace in each iteration and evaluated the reliability using the normalized mean-square errors among the wavelets estimated in different iterations.

Sequence architecture, depositional system evolution and controlling factors in a lacustrine basin: a case study from Changdi Area, Bohai Bay Basin, Eastern China

AbstractThe Paleogene Shahejie and Dongying formations of the eastern Bohai Bay Basin contain >500 m fluvial-deltaic deposits and >600 m deltaic-lacustrine deposits, both of which are important for oil and gas exploration. This study aims to figure out the deposition system distribution within the sequence stratigraphy and predict the exploration target. Sequence boundaries are recognized by unconformities in the seismic profiles and abrupt changes in the well-logging curves (acoustic, spontaneous potential, natural gamma-ray and resistivity), and two second-order and five third-order sequences (SQs1, SQs3 in the Shahejie Formation and SQd1-SQd3 in Dongying Formation) are identified. Four types of depositional system and distribution are determined by well-logging profiles, cores, relative sand content and root-mean-square seismic attributes. The SQs3 sequence is predominantly fan deltas and lacustrine deposits, the SQs1 sequence is predominantly shallow lacustrine deposits, the SQd3 sequence is predominantly braided-river delta, semi-deep lacustrine, and turbidite fan, and the SQd2 sequence is predominantly braided-river delta deposits, whereas the SQd1 sequence is predominantly braided-river deposits. The controlling factors are determined by basin subsidence simulation and fossil data of biological palynology, indicating that tectonic activity is the main controlling factor in SQs3 and SQd3 while climate is the main controlling factor in the SQs1, SQd2 and SQd1 sequences. Combined with the physical properties of sandstone, it is concluded that the beach bar in the SQs1 sequence, turbidite fan in the SQs3 and SQd3 sequences, and thick sandstone in the SQd2 sequence are the important exploration targets.

A study of inter-stratum propagation of hydraulic fracture of sandstone-shale interbedded shale oil

The vertical production layer of a shale oil reservoir in the Songnan Region, China, is mainly sandstone-shale interbed, and the reservoir thickness is uneven. As a result of varying interlayer properties, the hydraulic fracture height is hard to increase, and the propagation pattern of the fracture is unclear. In this paper, features of sandstone-shale lithological combination in the Songnan Region were explored by indoor rock mechanics experiments, which clarified the rock mechanics parameters and characterized the anisotropic features. Combined with the well-logging curves, the dynamic and static elastic moduli, Poisson's ratio, and tectonic coefficient were obtained. An evaluation model for crustal stress in the Songnan Region of China was built based on the hydraulic fracturing data. Additionally, a numerical model for 3D propagation of hydraulic fracture was established based on the finite element method to obtain three forms of fracture propagation and the effects of eight parameters, including fracturing fluid injection rate, interface strength, vertical stress, horizontal stress, elastic modulus, etc., on the height and length of hydraulic fracture were investigated. The results showed that increased viscosity and vertical stress of fracturing fluid could effectively increase fracture propagation height, low interface strength, fracturing fluid injection rate, and maximum and minimum horizontal principal stresses, thus favoring the propagation of hydraulic fractures. This study provides some guidance for the efficient development of sandstone-shale interbedded reservoir fracturing.

Active Domain Adaptation With Application to Intelligent Logging Lithology Identification.

Lithology identification plays an essential role in formation characterization and reservoir exploration. As an emerging technology, intelligent logging lithology identification has received great attention recently, which aims to infer the lithology type through the well-logging curves using machine-learning methods. However, the model trained on the interpreted logging data is not effective in predicting new exploration well due to the data distribution discrepancy. In this article, we aim to train a lithology identification model for the target well using a large amount of source-labeled logging data and a small amount of target-labeled data. The challenges of this task lie in three aspects: 1) the distribution misalignment; 2) the data divergence; and 3) the cost limitation. To solve these challenges, we propose a novel active adaptation for logging lithology identification (AALLI) framework that combines active learning (AL) and domain adaptation (DA). The contributions of this article are three-fold: 1) the domain-discrepancy problem in intelligent logging lithology identification is first investigated in this article, and a novel framework that incorporates AL and DA into lithology identification is proposed to handle the problem; 2) we design a discrepancy-based AL and pseudolabeling (PL) module and an instance importance weighting module to query the most uncertain target information and retain the most confident source information, which solves the challenges of cost limitation and distribution misalignment; and 3) we develop a reliability detecting module to improve the reliability of target pseudolabels, which, together with the discrepancy-based AL and PL module, solves the challenge of data divergence. Extensive experiments on three real-world well-logging datasets demonstrate the effectiveness of the proposed method compared to the baselines.

Well-Logging Prediction Based on Hybrid Neural Network Model

Well-logging is an important formation characterization and resource evaluation method in oil and gas exploration and development. However, there has been a shortage of well-logging data because Well-logging can only be measured by expensive and time-consuming field tests. In this study, we aimed to find effective machine learning techniques for well-logging data prediction, considering the temporal and spatial characteristics of well-logging data. To achieve this goal, the convolutional neural network (CNN) and the long short-term memory (LSTM) neural networks were combined to extract the spatial and temporal features of well-logging data, and the particle swarm optimization (PSO) algorithm was used to determine hyperparameters of the optimal CNN-LSTM architecture to predict logging curves in this study. We applied the proposed CNN-LSTM-PSO model, along with support vector regression, gradient-boosting regression, CNN-PSO, and LSTM-PSO models, to forecast photoelectric effect (PE) logs from other logs of the target well, and from logs of adjacent wells. Among the applied algorithms, the proposed CNN-LSTM-PSO model generated the best prediction of PE logs because it fully considers the spatio-temporal information of other well-logging curves. The prediction accuracy of the PE log using logs of the adjacent wells was not as good as that using the other well-logging data of the target well itself, due to geological uncertainties between the target well and adjacent wells. The results also show that the prediction accuracy of the models can be significantly improved with the PSO algorithm. The proposed CNN-LSTM-PSO model was found to enable reliable and efficient Well-logging prediction for existing and new drilled wells; further, as the reservoir complexity increases, the proxy model should be able to reduce the optimization time dramatically.

A New Approach for Blind Nonlinear Acoustic Impedance Inversion

We propose a blind nonlinear acoustic impedance inversion method. The seismic wavelet is first extracted through the Euclid deconvolution method from multichannel seismic data. Then, the acoustic impedance is inverted based on the exact nonlinear forward operator. The conventional Euclid deconvolution can theoretically estimate the reflectivity without special prior assumptions, but the method is extremely inefficient and unstable in the case of a large amount of data. We optimize the method and propose a frequency-domain algorithm to improve its efficiency. Conventional impedance inversions are almost implemented based on the linearized approximate formula, but the inversion errors will increase sharply when there is a strong reflection interface. We build the inversion objective function by the accurate nonlinear formula to improve accuracy. The total variation (TV) regularization and low-frequency components of well-logging curves are added to the objective function to make the inversion result have a block structure and converge to the absolute impedance. The nonlinear optimization problem is finally solved by the Levenberg–Marquardt (LM) algorithm. The results of synthetic data and field data verify that our method has high accuracy and good practicability.

A weighted fuzzy process neural network model and its application in mixed-process signal classification

A weighted fuzzy process neural network (WFPNN) model, and its corresponding classification algorithm, are proposed for quantitative and qualitative mixed-process signal classification problems. Based on predicate logic, fuzzy process neurons (FPN) are expressed as weighted fuzzy process inference rules on the semantic. Among them, the premise and conclusion are fuzzy predicates defined on a fuzzy set containing process information. Considering the applicability of rules to practical problems, confidence factor and applicable threshold are introduced to make the rules based on FPN have adaptive application ability. Based on FPN, the WFPNN was constructed with a process signal input layer, an FPN hidden layer, and a Takagi-Sugeno fuzzy classifier. It can integrate the learning properties and classification mechanisms of process neural networks (P-NNs) for time-vary signals with the logical inference abilities of fuzzy systems. So then to realize the quantitative and qualitative mixed process signal processing and direct classification. This study proposes a fusion analysis model that combining fuzzy decision theory with the process information processing method of P-NNs. It can automatically deduce the membership function and the fuzzy predicate logic rules from a set of given training examples, quickly build a fuzzy expert system prototype. And it can provide a new method for the construction and application of an expert system based on models or rules. Meanwhile, due to the adaptive learning property of WFPNN and the representation mechanism of inference rules based on the FPN, it is easy to realize the extension of the rule bases, model bases and application fields of the expert systems. This paper analyzes the properties of WFPNN, and establishes a corresponding learning algorithm. The discrimination of reservoir water-flooded states based on well-logging curves was used as an example for experimental analysis. The continuous measurement values of five physical quantities, such as resistivity, acoustic and radioactivity levels in the oil layer, were taken as input. The output was the discriminating result for four types of water-flooded states. The accuracy of the proposed method was 82.5%. Our accuracy was more than 7% higher than other existing automatic identification methods. These results demonstrate the viability and effectiveness of the proposed model and algorithms.

Quantitative identification of coal texture using the support vector machine with geophysical logging data: A case study using medium-rank coal from the Panjiang, Guizhou, China

Coal texture is important for predicting coal seam permeability and selecting favorable blocks for coalbed methane (CBM) exploration. Drilled cores and mining seam observations are the most direct and effective methods of identifying coal texture; however, they are expensive and cannot be used in unexplored coal seams. Geophysical logging has become a common method of coal texture identification, particularly during the CBM mining stage. However, quantitative methods for identifying coal texture based on geophysical logging data require further study. The support vector machine (SVM), a machine-learning method, has received great interest due to its remarkable generalization performance, and it has been used to quantitatively identify hard and soft coal using geophysical logging data. In this study, four well-logging curves, the acoustic time difference (AC), caliper log (CAL), density (DEN), and natural gamma (GR), were used for coal texture analysis. Hard coal (undeformed and cataclastic coal) exhibited higher DEN, GR, lower CAL, and lower AC than soft coal. The accuracy rate of coal texture identification was highest (97%) when the linear kernel function was applied, and the maximum training accuracy rate was achieved when the penalty parameter value of the linear kernel increased to 1. The results of verification with a newly cored CBM exploration well indicated that the SVM-based identification method was effective for coal texture analysis. With the increasing availability of data, this method can be used to distinguish hard and soft coal in a coal-bearing basin under numerous sample learning conditions.

A mathematical method to identify and forecast coal texture of multiple and thin coal seams by using logging data in the Panguan syncline, western Guizhou, China

There are abundant coalbed methane (CBM) resources in the western Guizhou Province, China. The high efficiency development of CBM in western Guizhou is restricted to a great extent by multiple and thin coal seams, the high in-situ stress and the complex coal texture. By using the mathematical principle of Linear Discriminant Analysis (LDA) method, 1508 logging data obtained from three CBM wells are related to their geophysical logging responses. Four types of well-logging curves of the natural gamma ray (GR), densities (DEN), acoustic logging (AC) and deep lateral resistivity (LLD) were chosen to analyze coal texture. The mathematical method reflecting the relationship between typical logging data and coal texture was established, which has a high accuracy for identifying the coal texture of medium-thin coal seams. The results show that tectonic coal in western Guizhou is developed and complicated. The coal texture predicted by the established method is in good agreement with that of actual coal core, which plays an important role in the joint and efficient development of CBM in the multiple and thin coal seams in western Guizhou.

Study of sedimentary characteristic in Denglouku formation, southern Songliao basin, Northeast China

Based on geological and geophysical data (e.g., core observation, well-logging curves, logging, analysis assay, and seismic data), the sedimentary facies, characteristics and distributions of the Lower Cretaceous Denglouku formation in the southeast uplift region of southern Songliao Basin are analyzed. The study shows that four types of sandbodies developed in Denglouku formation: alluvial fan, fan delta, braided river and braided river delta. In the early stage of the transition period named D1 period, the deposition is significantly controlled by the fault depression, resulting in rapid unloading of the sediments. Five large-scale divided fault-depressions and two small fault depressions have been developed. The ancient uplift at the periphery of the fault depression refers to the main development area of alluvial fan depositional system, Fan delta deposits are distributed around the fault depression, and the fan delta front extends into the lake. During D2 (the end of the transition period), several geological processes exist: (1) the alluvial fan, fan delta, braided river, braided river delta and lake sediments developed; (2) the unified sediment unloading area is formed. Taking the community of the floodplain and shore-shallow lake contact zone as a boundary, the western part of the boundary mainly developed alluvial fan and braided river sedimentary system from the provenance highland in the western and northern, the east part mainly developed lacustrine facies massively. With the debris sediments of provenance highland injected into the lake basin successively, it constituted retrograde fan deltas in different scales. Among them, the fan delta front and braided delta front subfacies are the main enrichment reservoirs for oil and gas, which is the favorable facies of oil and gas exploration in the Denglouku formation in the future.

The identification of coal texture in different rank coal reservoirs by using geophysical logging data in northwest Guizhou, China: Investigation by principal component analysis

Coal texture properties are one of important factors for determining the gas sorption capacity and transport properties. The recognition of coal texture with drilled cores or mining seam observation is direct and effective methods, but both methods are expensive and impossible for unexplored coal seams. The cut-off values logging data were applied in a few coal basins while the method ignores the effect of different coal ranks and is complicated with blurry boundary. In this study, 174 coal cores data obtained from 18 CBM wells were correlated with their geophysical logging responses in northwestern Guizhou Province. Four well-logging curves of the caliper logging (CAL), densities (DEN), natural gamma (GR) and deep lateral resistivity (LLD) were chosen to analyze coal textures. With progressive damage of coal, both values of CAL and LLD gradually increase while the DEN and GR tend to decrease. The identification index was reconstructed by using the principal component analysis (PCA) to identify coal texture of different coal ranks to improve the qualities of coal texture identification and reduce multiple solutions. The logging evaluation method for coal texture identification were applied in multiple coal seams in western Guizhou Province to validate the prediction method of logging data. The results show that PCA is feasible tool to analyze coal texture with improving accuracy and the well logging identification coal texture is good consistency with core identification in different rank coal.

Mechanism and Distribution of Calcareous Interbeds in Songtao Uplift and Its Periphery of Qiongdongnan Basin

Based on core and casting slice observation, well drilling and logging data, the source of carbonate materials, lithology together with electric properties, types, genesis mechanism and distribution of calcareous interbeds in Songtao uplift and its periphery of Qiongdongnan Basin have been thoroughly analyzed. Results show that typical features have been appeared from well logging curves, containing low gamma-ray, low acoustic travel time, low neutron value, high density, as well as bright white calcium nodules or bands in electrical imaging well-logging curves. Drilling results reveal that calcareous interbeds developed mostly in high position of paleostructures and their distribution was controlled by the combined effects of macroscopic and microscopic factors. Macroscopically, calcareous interbeds relate to paleogeomorphology together with the combination of sandstone and mudstone. They are also controlled microscopically by the source of carbonate cements and pore space. Under normal circumstances, with regard to the same sand, the closer to the mudstone and the thicker of mudstone, the more conducive to the formation of calcareous interlayer. Low compaction strength, high content of rigid particles, coarse grain size, well-sorted sandstone and large pore space during carbonate cementation are favorable for the development of calcareous interbeds.

Evaluation and genetic analysis of coal structures in deep Jiaozuo Coalfield, northern China: Investigation by geophysical logging data

Coal structure determines the porosity and permeability in coal reservoirs to a great extent, which not only influences the evaluation of coalbed methane (CBM) reservoirs, but is also closely related to their production efficiency. The traditional methods for identifying coal structure mainly depend on coals in boreholes or underground roadway sections. However, as these methods are expensive and limited to certain conditions, the geophysical logging data have been proven recently to be effective for detecting coal structure in boreholes. The Machang exploration block is situated in the southeastern (deep-seated) part of the Jiaozuo Coalfield, northern China, and the coals in this block have a higher gas content ranging from 25m3/t to 40m3/t but are typified with complex coal structures. Based on the field observation of coal structures for a cumulative length of 447.5m of cores from the 75 boreholes in the Machang block, the cut-off values of the logging data for quantitatively identifying coal structures (including lump coal, lump-powder coal and powder coal) were established using the three well-logging curves of deep lateral resistivity (LLD), gamma-gamma log (GGL) and natural gamma (GR). Subsequently, the relationships of coal structure with the corresponding coal depth, thickness, coal petrology, pore structures were investigated. Finally, the favorable areas for CBM exploration were evaluated according to the distribution of lump, lump-powder and powder coals. The results show that each coal structure has different logging characteristics, with lump coals showing lower GGL, higher GR and LLD, lump-powder coals of medium GGL, GR and LLD, while powder coals showing higher GGL, lower GR and LLD. The coal seam is composed of one to six sub-layers vertically, with a typical combination of powder coals on the top and bottom of the 21 coal seam, with lump coals or lump-powder coals in the middle part. Regional faults play a dominant role in controlling the distribution of coal structures, with the thickness of powder coals decreasing rapidly with increasing distance from faults. Furthermore, thicker or thinner coal seams also can result in a greater degree of damage during coal deformation. From lump coals to powder coals, the vitrinite content, maximum vitrinite reflectance (Ro,max), specific surface area and total pore volume show an upward trend but the average pore size shows a downward trend. The 4 types and 13 sub-types of coal structures were divided based on the established ternary diagram. The central parts in the South and Middle areas, together with the southwestern part of the North area in the Machang exploration block are determined to be the best regions for CBM production.

Enrichment and distribution of shale oil in the Cretaceous Qingshankou Formation, Songliao Basin, Northeast China

The Songliao Basin is a large-scale petroliferous basin in China. With a gradual decline in conventional oil production, the exploration and development of replacement resources in the basin is becoming increasingly important. Previous studies have shown that the Cretaceous Qingshankou Formation (K2qn) has favorable geological conditions for the formation of shale oil. Thus, shale oil in the Qingshankou Formation represents a promising and practical replacement resource for conventional oil. In this study, geological field surveys, core observation, sample tests, and the analysis of well logs were applied to study the geochemical and reservoir characteristics of shales, identify shale oil beds, build shale oil enrichment models, and classify favorable exploration areas of shale oil from the Cretaceous Qingshankou Formation. The organic matter content is high in shales from the first member of the Cretaceous Qingshankou Formation (K2qn1), with average total organic carbon (TOC) content exceeding 2%. The organic matter is mainly derived from lower aquatic organisms in a reducing brackish to fresh water environment, resulting in mostly type I kerogen. The vitrinite reflectance (Ro) and the temperature at which the maximum is release of hydrocarbons from cracking of kerogen occurred during pyrolysis (Tmax) respectively range from 0.5% to 1.1% and from 430 °C to 450 °C, indicating that the K2qn1 shales are in the low-mature to mature stage (Ro ranges from 0.5% to 1.2%) and currently generating a large amount of oil. The favorable depth for oil generation and expulsion is 1800–2200 m and 1900–2500 m, respectively as determined by basin modeling. The reserving space of the K2qn1 shale oil includes micropores and mircofractures. The micropore reservoirs are developed in shales interbedded with siltstones exhibiting high gamma ray (GR), high resistivity (Rt), low density (DEN), and slightly abnormal spontaneous potential (SP) in the well-logging curves. The microfracture reservoirs are mainly thick shales with high Rt, high AC (acoustic transit time), high GR, low DEN, and abnormal SP. Based on the shale distribution, geochemical characteristics, reservoir types, fracture development, and the process of shale oil generation and enrichment, the southern Taikang and northern Da'an are classified as two favorable shale oil exploration areas in the Songliao Basin.

Management System for Well-Logging Data Curves Based on Delphi

The system for well-logging data designed for data files unified management, can offer a function based on data query and download, also can offer instant replays for data curves. This system contains some functions following that format converting, uploading, query, download and preview for well-logging data curves. All system is designed by software Delphi GDI+.