Logarithmic Curve Research Articles

Development and controlling factors of shale lithofacies cycles in a continental rift basin: A case study of Es4u in the Boxing Subsag of Dongying Sag, Bohai Bay Basin, China

The shale of the upper Es4 formation (Es4u), deposited during the Eocene in the Boxing Subsag of the Dongying Sag, is a typical set of lime-rich lacustrine shale in a continental rift basin. Through logging data interpretation, core and thin section observations, and geochemical elements [obtained by X radial fluorescence (XRF) mud logging] analysis, the development and controlling factors of lithofacies cycles of Es4u shale were analyzed. The results show that 22 types of lithofacies with typical characteristics are developed in Es4u, indicating the sedimentary environments, such as lower lakeshore (LL) slope zone; upper, middle, and lower shallow-lake slope zones; semi-deep-lake zone; and deep-lake zone. Lithofacies cycles in three scales are recognized in Es4u: small-scale lithofacies cycles indicated by alternate development of two lithofacies, mesoscale lithofacies cycles indicated by the repetition of lithofacies associations or regular variation of multiple lithofacies, and large-scale lithofacies cycles indicated by regular variation of well logging curves, which have the average thickness of approximately 0.4, 5, and 35 m, respectively. According to the vertical variation of astronomical parameters and lithofacies cycles, the periodic change in long eccentricity has a close relationship with the large-scale lithofacies cycle, the periodic change in slope is closely relative to the mesoscale lithofacies cycle, and the periodic change in precession is closely related to small-scale lithofacies cycle. However, the periodic change in short eccentricity correlates poorly with lithofacies cycles. The climate change reflected by long eccentricity and slope is quite distinct from the actual basin climate reflected by the paleoclimatic parameters (e.g., Rb/Sr and Sr/Cu), indicating that the basin paleoclimate was greatly influenced by the paleogeological conditions. For shale in Es4u of the Boxing Subsag, approximately 55% of the shale stratigraphic record is dominantly controlled by the astronomical climate and 45% by the paleogeological conditions. Affected by the paleogeological conditions, large-scale and mesoscale lithofacies cycles are different in lithofacies types and superimposition patterns, and just small-scale lithofacies cycles are found repetitive vertically. These findings suggest that the lithofacies cycles of shale in a continental rift basin are a product of astronomical driving mechanism (Milankovitch forcing) and variation of local paleogeological conditions, which is apparently different from the sedimentary record of marine shale that is evidently controlled by the Milankovitch cycle. Therefore, the lithofacies cycles should be comprehensively analyzed under the background of actual basin paleoclimate.

Methodological progress note: Machine learning methods in healthcare research.

Methodological progress note: Machine learning methods in healthcare research.

Imprecision remains to be improved in the measurement of serum cystatin C with heterogeneous systems.

Except for the large bias of some measurement systems for serum cystatin C (CysC) measurements, unacceptable imprecision has been observed for the heterogenous system. This study analyzed the external quality assessment (EQA) results in 2018-2021 to provide an insight into the imprecision of CysC assays. Five EQA samples were sent to participating laboratories every year. Participants were divided into reagent/calibrator-based peer groups, for which the robust mean of each sample and robust coefficient of variation (CV) were calculated by Algorithm A from ISO 13528. Peers with more than 12 participants per year were selected for further analysis. The limit of CV was determined to be 4.85% based on clinical application requirements. The concentration-related effect on CVs was investigated using logarithmic curve fitting; the difference in medians and robust CVs between instrument-based subgroups was also evaluated. The total number of participating laboratories increased from 845 to 1,695 in four years and heterogeneous systems remained the mainstream (≥85%). Of 18 peers with≥12 participants, those using homogeneous systems showed relatively steady and small CVs over four years, with the mean four-year CVs ranging from 3.21 to 3.68%. Some peers using heterogenous systems showed reduced CVs over four years, while 7/15 still had unacceptable CVs in 2021 (5.01-8.34%). Six peers showed larger CVs at the low or high concentrations, and some instrument-based subgroups presented greater imprecision than others. More efforts should be made to improve theimprecision of heterogeneous systems for CysC measurement.

Sequence stratigraphic architecture, evolution, and geological controls of an Eocene isolated carbonate platform, NE Sirte Basin, Libya

The sequence stratigraphic architecture, evolution, and controlling mechanisms of a lower and middle Eocene isolated carbonate platform in the Sirte Basin in north-central Libya were interpreted from two- and three-dimensional (2D and 3D) seismic and well data. The platform contains six stratigraphic surfaces that divide the platform sedimentary successions into two stratigraphic sequences, namely Sequence 5 and Sequence 6, and five systems tracts. The five systems tracts vary in thickness and differ in depositional systems. The lowstand systems tract (LST) of Sequence 5 forms at the base of the focus interval and is composed of a basin floor fan system with hummocky to undulated seismic facies. The highstand systems tracts (HST) of Sequence 5 has the greatest thickness and comprises a well-defined slope system showing a funnel-shaped trend and suggesting progradational stacking pattern. The LST of Sequence 6 contains a middle ramp system formed during the lowest sea-level. The two transgressive systems tracts (TST) are the thinnest, several meters thick, and separating the LST from the HST. Log curves show an overall funnel-shaped motif through all three depositional systems, indicating a progradational stacking pattern. The main factor controlling the platform growth and demise is sea-level fluctuations. Additional factors, such as, subsidence, antecedent topography, and prevailing currents have also contributed to the evolution of the platform. Sea-level changes coupled with subsidence create accommodation for the platform growth and terminate deposition by drowning. The pre-existing topography creates a topographic relief and influences the growth pattern of the platform. Prevailing local currents influence the direction of platform progradation. This study provides insights on the evolution of isolated carbonate platform in a rift setting, which can help predict the lithofacies and depositional systems of economic significance.

Multichannel seismic impedance inversion based on Attention U-Net

Recently, seismic inversion has made extensive use of supervised learning methods. The traditional deep learning inversion network can utilize the temporal correlation in the vertical direction. Still, it does not consider the spatial correlation in the horizontal direction of seismic data. Each seismic trace is inverted independently, which leads to noise and large geological variations in seismic data, thus leading to lateral discontinuity. Given this, the proposed method uses the spatial correlation of the seismic data in the horizontal direction. In the network training stage, several seismic traces centered on the well-side trace and the corresponding logging curve form a set of training sample pairs for training, to enhance the lateral continuity and anti-noise performance. Additionally, Attention U-Net is introduced in acoustic impedance inversion. Attention U-Net adds attention gate (AG) model to the skip connection between the encoding and decoding layers of the U-Net network, which can give different weights to different features, so the model can focus on the features related to the inversion task and avoid the influence of irrelevant data and noise during the inversion process. The performance of the proposed method is evaluated using the Marmousi2 model and the SEAM model and compared with other methods. The experimental results show that the proposed method has the advantages of high accuracy of acoustic impedance value inversion, good transverse continuity of inversion results, and strong anti-noise performance.

Lithology identification using graph neural network in continental shale oil reservoirs: A case study in Mahu Sag, Junggar Basin, Western China

The continental shale oil reservoir of Fengcheng Formation in the northern slope area of Mahu Sag, Junggar Basin, Western China is very heterogeneous in lithology. Thus, the complex response characteristics of conventional logging and limited core availability in the study area has led to major challenges in lithology identification. Therefore, to resolve lithology identification by well logs in continental shale oil reservoirs, a graph neural network (GNN) method named GraphSAGE is used to train the lithology identification model based on a constructed graph, which connects the samples with adjacent depth and similar log response features on operator intention. The identification process is divided into two parts: first, based on the formation depth sequence and affinity propagation clustering method, the vertical distribution of the stratum and nodes logging curve similarity information are integrated into the graph structure, which structurally represents the conventional logging curves as graph instead of well logs as input data; Second, the nodes of the constructed graph are classified by GraphSAGE, which naturally supports combination generalization and improves sample complexity accompanied by strong relational inductive bias. To examine the effectiveness of GraphSAGE for lithology identification, a conventional log dataset labelled by direct core observations from two separate wells in Muhu Sag are used. The identification results showed that the accuracy of GraphSAGE for the lithologies exceeds 90% of the testing data, especially for transitional lithology such as dolomitic mudstone, silty mudstone and tuffaceous fine sandstone. Compared with the commonly used machine learning methods such as SVM, RF and XGBoost, GraphSAGE was more accurate in lithology identification, matching core observations. Collectively, this reflects the superiority of graph neural network in conventional logging lithology identification and effective means provided for lithology identification of continental shale oil reservoir in the Mahu Sag.

Cross-Well Lithology Identification Based on Wavelet Transform and Adversarial Learning

For geological analysis tasks such as reservoir characterization and petroleum exploration, lithology identification is a crucial and foundational task. The logging lithology identification tasks at this stage generally build a lithology identification model, assuming that the logging data share an independent and identical distribution. This assumption, however, does not hold among various wells due to the variations in depositional conditions, logging apparatus, etc. In addition, the current lithology identification model does not fully integrate the geological knowledge, meaning that the model is not geologically reliable and easy to interpret. Therefore, we propose a cross-domain lithology identification method that incorporates geological information and domain adaptation. This method consists of designing a named UAFN structure to better extract the semantic (depth) features of logging curves, introducing geological information via wavelet transform to improve the model’s interpretability, and using dynamic adversarial domain adaptation to solve the data-drift issue cross-wells. The experimental results show that, by combining the geological information in wavelet coefficients with semantic information, more lithological features can be extracted in the logging curve. Moreover, the model performance is further improved by dynamic domain adaptation and wavelet transform. The addition of wavelet transform improved the model performance by an average of 6.25%, indicating the value of the stratigraphic information contained in the wavelet coefficients for lithology prediction.

Automated Well-Log Pattern Alignment and Depth-Matching Techniques: An Empirical Review and Recommendations

Well logging has been an integral part of decision making at different stages (drilling, completion, production, abandonment) of a well’s history. However, the traditional human-reliant approach to well-log interpretation, which has been the most common practice in the industry, can be time consuming, subjective, and incapable of identifying fine details in log curves. Previous studies have recommended automated approaches as a candidate for addressing these challenges. Despite the progress made so far, what is not yet clear from the existing literature is the extent to which these automated approaches can dispense with human interventions in real-life scenarios. This paper presents an empirical review of different depth-matching techniques in real-life timelapse well logs, primarily focusing on gamma ray and the extent to which the outcomes of these techniques match the results from a human expert. Specifically, the performances of dynamic time warping (DTW), constrained DTW (CDTW), and correlation optimized warping (COW) are investigated. The experiments also consider the effects of filtering and normalization on the performance of each of the techniques. Concerning the correlations of each technique’s outcome with the reference data and an expert-generated outcome, this research identifies and discusses its key challenges, as well as provides recommendations for future research directions. Although the COW technique has its limitations, as discussed in this paper, our experiments demonstrate that it shows more potential than DTW and its variants in the well-log pattern alignment task. The work entailed by this research is significant because identifying and discussing the limitations of these techniques is vital for solution-oriented future research in this area.

Research on distribution characteristics of sedimentary microfacies of a system tract under a high resolution sequence framework: A case study of Qixia Formation in Gaomo block, central Sichuan

In conventional sequence stratigraphy research, owing to the limitation of the resolution of seismic data, strata can only be divided into third order sequences at most on the seismic profile. In this study, the vertical fourth order sequence is divided using the high vertical resolutions of gamma ray (GR), uranium (U), thorium (Th), and potassium (K) curves in the logging curves, and the sequence divided in the depth system is then accurately superimposed on the seismic profile through one dimensional forward modelling. Subsequently, through three dimensional Fourier transform technology, the dip angle and azimuth information of the seismic data in the entire region are scanned to obtain the horizons. Through the progradation and retrogradation characteristics of sediments in the Wheeler system, guided by the sequence framework, the horizontal interpretation of the fourth order sequence system tract is conducted in the entire region. Finally, through attribute optimisation, the root mean square amplitude attribute, with the highest degree of coincidence with well data, is selected to study the evolution characteristics of sedimentary microfacies in different periods in the four system tracts. Notably, the strata of Qixia Formation in the study area can be divided into two-fourth order sequences, wherein SQ1 comprised transgressive systems tract 1 (TST1) and high stand systems tract 1 (HST1), and SQ2 comprised TST2 and HST2. With the change in the relative sea level cycle, the strata of Qixia Formation in the study area are developed by overlapping the sedimentary microfacies of the interbank depression, interbank sea, and intraplatform shoal from bottom to top.

Removing different number of regional lymph nodes affects survival outcomes of operable patients at stage IIA non-small cell lung cancer (according to the 8th edition staging).

Surgery combined with chemotherapy (CT) is the best treatment for tumor patients at stage I to IIIA. But there are only few studies specifically evaluated the survival benefits of removing different number of regional lymph nodes (RLNs) for patients with stage IIA non-small cell lung cancer (NSCLC). The objective of this study is to discuss the effect of removing different number of RLNs on survival outcomes in operable patients at stage IIA NSCLC. Through the use of the Surveillance, Epidemiology, and End Results (SEER) registry, satisfactory patients at stage IIA NSCLC, who had complete clinical information from 2004 to 2015, were identified. Lung cancer-specific survival (LCSS) and overall survival (OS) were compared by the Kaplan-Meier analysis and Cox regression analyses to determine the impact of the confounding factors on the survival outcomes. LCSS and OS as the primary endpoints were compared among patients with different number of RLNs removed. A total of 3,362 patients at stage IIA NSCLC met our criteria, including 173 (5.1%), 486 (14.5%), 2,703 (80.4%) patients without RLNs removed, with 1 to 3 RLNs removed and with greater than or equal to 4 RLNs removed, respectively. Kaplan-Meier survival analyses and Univariate Cox regression analyses revealed that there was a statistically significant difference on survival curve (log rank P<0.001) among the stage IIA NSCLC patients with different number of RLNs removed. Furthermore, multivariable Cox regression analyses on LCSS showed that the hazard ratio (HR) and 95% confidence interval (95% CI) of the 1 to 3 RLNs removed group and greater than or equal to 4 RLNs removed group were 0.622 (0.484-0.800, P<0.001) and 0.545 (0.437-0.680, P<0.001), respectively, compared to without any RLNs removed group. This study illustrated that removing different number of RLNs can affect survival outcomes of operable patients at stage IIA NSCLC. Whether more radical lymphadenectomy is beneficial to patients at stage IIA NSCLC still needs to be researched.

P-wave prediction method under multi-source spatiotemporal feature fusion and physics-informed neural network

Compressional wave velocity (P-wave) plays an important role in seismic amplitude versus offset (AVO) forward and inversion modeling and reservoir prediction. However, due to the high cost and difficulty of P-wave logging, the actual acquired P-wave data is limited, which generally does not suffice for practical applications. To provide reliable data support for oil and gas reservoir exploration, a P-wave prediction method under multi-source spatiotemporal feature fusion and physics-informed neural network (MSTFPIN) is proposed. First, a spatiotemporal hybrid model (STHN) is constructed by combining convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) neural network to accurately express the spatial and temporal characteristics of the logging curves, which utilizes the local perception ability of CNN and long short-term memory function of BLSTM to mine their local shape and depth trends. Then, seismic data are introduced as an additional input of multi-source information, which fills the gap of inter-well geological information, and feature fusion is used to fully exploit the information from multiple sources to enhance the available data. Finally, a physics-informed neural network (PINN) is constructed based on the idea of seismic forward modeling in petroleum engineering to guide the training process of the prediction model, which ensures that the prediction results are consistent with geological laws. P-wave prediction experiments are conducted using real logging and seismic data to verify the effectiveness and generalization ability of the proposed method compared with other methods. The experimental results show that the proposed MSTFPIN model exhibit better prediction performance, and the correlation coefficient and coefficient of determination reach 0.9619 and 0.9234, respectively. Compared with traditional empirical and regression models, the proposed MSTFPIN exhibited higher accuracy and generalization. Overall, our results demonstrate the proposed method can be implemented in real oil fields to improve the accuracy of reservoir prediction.

Dynamic Simulation Analysis of the Working Process of the Picking Mechanism of a Sugarcane Leaf Cutting and Returning Machine

Leaf–device interaction can effectively be modeled with a finite element model when proper finite element model parameters are applied. In order to investigate the contact mechanism of picking up sugarcane leaf during the operation of a sugarcane leaf cutting and returning machine, a geometric solid model of sugarcane leaf picking was established. A finite element numerical model to analyze the large deformation problem of flexible bodies was developed in LS-DYNA to simulate the picking process of the returning machine. A dynamic simulation of the sugarcane leaf-picking process was carried out to obtain the change of stress field and the motion posture of the sugarcane leaf and the elastic teeth. The picking process of the picking mechanism, the change in posture of the sugarcane leaves, the change in stress on the sugarcane leaf, the change in the bending angle of the sugarcane leaf and the change in stress on the elastic teeth were analyzed in detail. The results showed that the picking process can be divided into four stages: picking, lifting, pushing and retrieving. The posture changes of sugarcane leaf are “C”, logarithmic curve, wavy shape and “V”, in turn. During the picking process, the sugarcane blade showed some breakage, the sugarcane vein remained intact, and the elastic teeth did not fail. During the whole picking cycle, the maximum Von Mises stress of the blade, vein and elastic teeth were 22.8 MPa, 17.5 MPa and 900 MPa, respectively. An evaluation criterion of bending angle was creatively put forward to measure the bending deformation of leaves. The trend in the sugarcane leaf bending angle shows that it is largely variable, gradually decreasing, fluctuating and increasing with interval fluctuations. The working process of the picking mechanism was observed through a quick camera experiment. Comparing the experiment with the simulation, the changing trend of the simulation data and experimental data was generally similar. The experimental and simulation values of the average sugarcane leaf bending angle were 27° and 19°, respectively. The relative error of the average bending angle was 29.6%. It was concluded that the developed finite element model is substantial and could be applied to optimize and improve the picking mechanism. In addition, some references were provided for the contact mechanism between the picking mechanism and the sugarcane leaf.

Prediction of hydrocarbon source rock distribution using logging curves: A case study of Es32 source rock in Nanpu Sag, Huanghua depression, Bohai Bay Basin

The Es3 is the main hydrocarbon source rock system in the Nanpu Sag. Finally, the TOC and hydrocarbon potential of each sub depression of Es3were predicted. The study shows that the hydrocarbon source rocks of Es32 and Es34 sections are mainly of type II2 and type II1 respectively, with good organic matter type and high maturity. Biomarker compound parameters indicate that the Es32 section hydrocarbon source rocks developed in a semi-saline, low to medium terrestrial source organic matter supplied reduction environment with a high algal contribution; the Es34 section hydrocarbon source rocks formed in a freshwater, low terrestrial source supplied reduction environment with a medium-high algal contribution. The multiple linear regression method is more effective than the ΔlgR method in predicting hydrocarbon source rocks in the Nanpu Sag, and the prediction accuracy is higher; the correlation between TOC and S1 + S2 is the best in the model for predicting hydrocarbon potential. The TOC and hydrocarbon potential of the hydrocarbon source rocks in Es31 are generally low; the high value area of TOC and hydrocarbon potential of the hydrocarbon source rocks in Es32 is partly between the No. 1 tectonic zone and No. 5 tectonic zone in Linque sub depression, and the TOC and hydrocarbon potential of the hydrocarbon source rocks in Liunan sub depression are larger; the high value area of TOC and hydrocarbon potential of the hydrocarbon source rocks in Es34 is mainly concentrated in Shichang sub depression.

Influence of curing stress on compression characteristic of cement-treated dredged slurry at high water contents

This paper presents an experimental investigation on the compression behavior of cement-treated dredged slurry at high water content considering the effect of applied curing stress. A series of one-dimensional compression tests were performed on treated samples at various water contents, cement contents, and curing times with and without applied curing stress. Tests result showed that the void ratio versus logarithmic loading pressure curve of all tested specimens exhibits the same decreasing pattern with an obvious yield phenomenon. The void ratio slightly reduces in the pre-yield stage while significantly decreasing with logarithmic loading pressure in the post-yield stage. The magnitude compression index C s in the pre-yield stage is much lower than that of the value C c in the post-yield stage. At the same water content and cement content, when the curing pressure is applied, the magnitudes of C s and C c are reduced. The yield stress of treated dredged clay samples cured at the same curing time with applied curing stress is higher than that without applied curing stress, indicating that the presence of curing stress has a positive effect on the acquisition of yield stress. It is an effective way to apply curing stress in the actual engineering practice.

Resistive switching characteristics of interfacial device based on Bi2Ti2O7 film

To solve the problem of information storage and explore a new generation of memory, Au/Bi2Ti2O7/NiO/ITO (Indium tin oxide semiconductor) resistive variable memory is fabricated and its resistive switching characteristics are studied. The device has diode-like rectifier characteristics and shows good durability and stability under 100 cycles of read−and−write tests. At the reading voltage of 0.44 V, the resistive switching ratio of the device reaches 102. Combining the O 1 s spectrum of XPS and further analyzing the I − V double logarithmic curve, it is found that the conduction mechanism of the device is mainly related to the ohmic conduction mechanism and the space charge limiting current mechanism, and its resistive switching behavior is related to the migration of oxygen vacancies to change the interface barrier.

Digital construction of geophysical well logging curves using the LSTM deep-learning network

A complete well logging suite is needed frequently, but it is either unavailable or has missing parts. The mudstone section is prone to wellbore collapse, which often causes distortion in well logs. In many cases, well logging curves are never measured, yet are needed for petrophysical or other analyses. Re-logging is expensive and difficult to achieve, while manual construction of the missing well logging curves is costly and low in accuracy. The rapid technical evolution of deep-learning algorithms makes it possible to realize the digital construction of missing well logging curves with high precision in an automated fashion. In this article, a workflow is proposed for the digital construction of well logging curves based on the long short-term memory (LSTM) network. The LSTM network is chosen because it has the advantage of avoiding the vanishing gradient problem that exists in traditional recurrent neural networks (RNNs). Additionally, it can process sequential data. When it is used in the construction of missing well logging curves, it not only considers the relationship between each logging curve but also the influence of the data from a previous depth on data at the following depth. This influence is validated by exercises constructing acoustic, neutron porosity, and resistivity logging curves using the LSTM network, which effectively achieves high-precision construction of these missing curves. These exercises show that the LSTM network is highly superior to the RNN in the digital construction of well logging curves, in terms of accuracy, efficiency, and reliability.

The Effect of Soil Preparation Methods on Rain Water Infiltration as The Basis of Irrigation Application for Dry Land Rice

The purpose of this study was to determine the effect of land preparation methods on the soil infiltration which can be used as a basis for designing irrigation for dryland rice cultivation. The study was arranged in a completely randomized design (CRD) with three land preparation treatments, namely T0 (no tillage), T1 (hoeing), and T2 (hoeing plus loosening). Each treatment was carried out with three replication plots. Artificial rain was given with an average intensity of 4.61 mm/h for 120 min. Observations were made on the infiltration thickness and rainwater volume. Results showed that land preparation methods resulted in very significant differences in the cumulative infiltration depth and infiltration rate, where the T2 treatment caused the highest infiltration. During 120 minutes of rain, 331.83 liters of water volume was poured out and resulted in an average infiltration thickness of 7.3 cm for T0 (no tillage), 18.09 cm for T1 (hoeing), and 21,3 cm for T2 (hoeing plus loosening). The results also showed that cumulative infiltration (y) increased with rain water volume (x) that followd a logarithmic curve with an R2 value of more 94-98% for the three land preparation methods with order T2 &gt; T1 &gt; T0.Keywords: Rain, Tillage, Infiltration, Dry land, Rice

Quantitative evaluation of reservoir quality of tight oil sandstones in chang 7 member of Ordos Basin

In order to establish a quantitative evaluation system for reservoir quality suitable for tight oil sandstones, in this study, taking the Chang 7 Member in the Maling area of the Ordos Basin as an example, the nuclear magnetic resonance, clay mineral analysis, high pressure mercury injection analysis and logging interpretation technology have been used to carry out a comprehensive evaluation of the pore structures, sand body structures and oil-bearing properties of tight oil sandstone reservoirs. The results show that the pseudo-capillary pressure curves transformed by the NMR T2 spectra are consistent with the capillary pressure curves measured by the core experiments. This method can be used for accurate characterization of the pore structures of the reservoir. The pore structure parameters calculated based on the pseudo-capillary pressure curves can accurately reflect the pore structures of the reservoirs such as micropores-thin throats and complex tortuosity. At the same time, the smoothness feature of conventional logging curves is used to evaluate the sand body structures and heterogeneity of the reservoir, and the apparent energy storage coefficient is introduced to quantitatively evaluate the oil-bearing properties of tight oil reservoirs. The evaluation results are in good agreement with the actual production situation. The larger the apparent energy storage coefficient, the higher the initial output of the oil wells. The evaluation results of the reservoir quality of the tight oil sandstones constructed in this paper are highly consistent with the production status, so the method has broad application prospects.

Investigation and prediction of pore type system by integrating velocity deviation log, petrographic data and mercury injection capillary pressure curves in the Fahliyan Formation, the Persian Gulf Basin

Investigation and prediction of pore type system by integrating velocity deviation log, petrographic data and mercury injection capillary pressure curves in the Fahliyan Formation, the Persian Gulf Basin

Application of the Wavelet Transform and INPEFA in Sequence Stratigraphy.

The strata of the Shan 2 Member of the Shanxi Formation of Permian(S2)in the LQ Gas Field in the northeast of the Ordos Basin restrict the study of sand body distribution and lateral distribution. The accuracy of sand body prediction is reduced, which greatly affects the exploration and development of oil and gas. In order to accurately characterize the transverse distribution of sand bodies and improve the prediction accuracy of sand bodies, S2 is divided by the sequence stratigraphy method. Research based on the traditional stratigraphic division method includes seismic, logging, and core data. The logging curve is processed by INPEFA and the wavelet transform to reduce artificial division interference. The range of the S2 section is limited by the seismic interpretation surface. The maximum entropy spectral analysis and the wavelet transform are used for fine division, and S2 is divided into 1 long-term base level cycle, 6 medium-term base level cycles, and 11 short-term base level cycles. The fine stratigraphic division improves the exploration accuracy of oil and gas reservoirs.