Well logging evaluation of fine-grained hydrate-bearing sediment reservoirs: Considering the effect of clay content

Hydrate reservoirs are different from the host reservoirs of all other fossil energy sources because the characteristics of hydrate reservoirs are generally controlled by deep-sea fine-grained sedimentation. In such reservoirs, the reliability of the classical logging evaluation models established for diagenetic reservoirs is questionable. This study used well W8 in the Qiongdongnan Basin to explore the clay content, porosity, saturation, and hydrate-enriched layer identification of a logging-based hydrate reservoir, and it was found that considering the effect of the clay content on the log response is necessary in the logging evaluation of hydrate reservoirs. In the evaluation of clay content, a method based on the optimization inversion method can obtain a more reliable clay content than other methods. Fine-grained sediment reservoirs have a high clay content, and the effect of clay on log responses must be considered when calculating porosity. In addition, combining density logging and neutron porosity logging data can obtain the best porosity calculation results, and the porosity calculation method based on sonic logging predicted that the porosity of the studied reservoir was low. It was very effective to identify hydrate layers based on resistivity, but the clay distribution and pore structure will also affect the relationship between resistivity, porosity and saturation, and it was suggested that the factors effecting the resistivity of different layers should be considered in the saturation evaluation and that a suitable model should be selected. This study also considered the lack of clarity of the relationships among the lithology, physical properties, hydrate-bearing occurrence properties, and log response properties of hydrate reservoirs and the lack of specialized petrophysical models. This research can directly help to improve hydrate logging evaluation.

Fractal Correction of Well Logging Curves

It is always significant for assessing and evaluation of oil bearing layers, especially for well logging data processing and interpretation of non marine oil beds to get more accurate physical properties in thin and inter thin layers. This paper presents a definition of measures and the measure presents power law relation with the corresponded scale described by fractal theory. Thus, logging curves can be reconstructed according to this power law relation. This method uses the local structure nearby concurrent points to compensate the average effect of logging probes and measurement errors. As an example, deep and medium induced conductivity (IMPH and IDPH) curves in ODP Leg 127 Hole 797C are reconstructed or corrected. Corrected curves are with less adjacent effects through comparison of corrected curves with original one. And also, the power spectra of corrected well logging curve are abounding with more resolution components than the original one. Thus, fractal correction method makes the well logging more resoluble for thin beds.

Data mining and well logging interpretation: application to a conglomerate reservoir

Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and- bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the C5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs.

Acoustic reflection well logging modeling using the frequency-domain finite-element method with a hybrid PML

In this paper, we propose a hybrid PML （H-PML） combining the normal absorption factor of convolutional PML （C-PML） with tangential absorption factor of Mutiaxial PML （M-PML）. The H-PML boundary conditions can better suppress the numerical instability in some extreme models, and the computational speed of finite-element method and the dynamic range are greatly increased using this HPML. We use the finite-element method with a hybrid PML to model the acoustic reflection of the interface when wireline and well logging while drilling （LWD）, in a formation with a reflector outside the borehole. The simulation results suggests that the PS- and SP- reflected waves arrive at the same time when the inclination between the well and the outer interface is zero, and the difference in arrival times increases with increasing dip angle. When there are fractures outside the well, the reflection signal is clearer in the subsequent reflection waves and may be used to identify the fractured zone. The difference between the dominant wavelength and the model scale shows that LWD reflection logging data are of higher resolution and quality than wireline acoustic reflection logging.

Acousto-electric well logging by eccentric source and extraction of shear wave

The nonaxisymmetric acousto-electric field excited by an eccentric acoustic source in the borehole based on Pride seismoelectric theory is considered. It is shown that the acoustic field inside the borehole, converted electric and magnetic fields and coupled fields outside the borehole are composed of an infinitude of multipole fields with different orders. The numerical results show that both the electromagnetic waves and the seismoelectric field in the borehole, and the three components of both electric field and magnetic field can be detected. Measurements on the borehole axis will be of advantage to determining shear velocity information. The components of the symmetric and nonsymmetric acoustic and electromagnetic fields can be strengthened or weakened by adding or subtracting the two full waveforms logged in some azimuths. It may be a new method of directly measuring the shear wave velocity by using the borehole seismoelectric effect.

Discrimination of Natural Fractures Using Well Logging Curve Unit

It is very difficult to discriminate natural fractures using conventional well log data, especially for most of the matured oilfields in China, because the raw data were acquired with relatively obsolete tools. The raw data include only GR and SP curves, indicative of lithology, AC curves, used to calculate the porosity of the formation, and a set of logging curves from various electrode length resistivity by laterolog. On the other hand, these oilfields usually have a large amount of core data which directly display the characteristics of the formation, and enough information of injection and production. This paper describes an approach through which logging curves are calibrated in terms of the raw data, and then a prototype model of natural fractures is established based on the investigation of core data from 43 wells, totaling 4 000 m in length. A computer program has been developed according to this method. Through analysis and comparison of the features of logging curves, this paper proposes a new concept, the well logging curve unit. By strictly depicting its shape through mathematical methods, the natural facture can be discriminated. This work also suggests an equation to estimate the probability of fracture occurrence, and finally other fracture parameters are calculated using some experimental expressions. With this methodology, logging curves from 100 wells were interpreted, the results of which agree with core data and field information.

CCSD Well Logging Engineering Program

This paper introduces briefly the tasks and characteristics of China Continent Science Drilling (CCSD) Well Logging Engineering, the logging methods measured with CCSD, the quality control of original logging information, the logging plan of CCSD, the logging engineering management of CCSD, the logging interpretation and the results and reports made with CCSD.

Simulation Experiments in Electromagnetic Tomography Well Logging in Two-phase Flow

Electromagnetic Computer Tomography （ECT） is a method to probe the interior of an inhomogeneous medium via surface measurement in a non-linear way. Due to the great differences in conductivity and permittivity between oil and water in the well, Electromagnetic Tomography Well Logging （ETWL）, a new flow imaging measurement system, is proposed to describe the distribution and movement of oil/water two-phase flow in the well by scanning the detected region and applying a suitable data processing algorithm. The results of the numerical simulation and physical modeling show that the system could provide a clear image of the flow profile.

Construction of well logging knowledge graph and intelligent identification method of hydrocarbon-bearing formation

Based on the well logging knowledge graph of hydrocarbon-bearing formation(HBF),a Knowledge-Powered Neural Network Formation Evaluation model(KPNFE)has been proposed.It has the following functions:(1)extracting characteristic parameters describing HBF in multiple dimensions and multiple scales;(2)showing the characteristic parameter-related entities,relationships,and attributes as vectors via graph embedding technique;(3)intelligently identifying HBF;(4)seamlessly integrating expertise into the intelligent computing to establish the assessment system and ranking algorithm for potential pay recommendation.Taking 547 wells encountered the low porosity and low permeability Chang 6 Member of Triassic in the Jiyuan Block of Ordos Basin,NW China as objects,80%of the wells were randomly selected as the training dataset and the remainder as the validation dataset.The KPNFE prediction results on the validation dataset had a coincidence rate of 94.43%with the expert interpretation results and a coincidence rate of 84.38%for all the oil testing layers,which is 13 percentage points higher in accuracy and over 100 times faster than the primary conventional interpretation.In addition,a number of potential pays likely to produce industrial oil were recommended.The KPNFE model effectively inherits,carries forward and improves the expert knowledge,nicely solving the robustness problem in HBF identification.The KPNFE,with good interpretability and high accuracy of computation results,is a powerful technical means for efficient and high-quality well logging re-evaluation of old wells in mature oilfields.

The Application of Well Logging to CBM Exploration

Well logging technology in coalbed methane （CBM） exploration may develop in two directions： one is the novel well logging methods; the other is the new interpretation methods for the conventional logging data, on which the authors of this paper concentrated mainly. The paper introduced several methods in calculating with well logs such important parameters as porosity, permeability and gas content of CBM reservoir and evaluated their effectiveness. A new method of well logging data interpretation was put forward for coalbed recognition, i.e., the combination of the principal component analysis and the wavelet transform. The authors find that the second principal component （PCA2） contains much more information of coalbed in the coal-bearing series and the reconstruction signal from the detailed wavelet coefficients at level 4 （PCA24） and 5 （PCA25） highlights the signature ofcoalbeds. In terms of the characteristics of CBM reservoir in China, the authors summarized the key points in the application of well logging technique to CBM exploration, and gave a guideline for further related research work.

A Brief Introduction of Jianghan Well Logging Institute

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Well Logging Responses of UHP Metamorphic Rocks from CCSD Main Hole in Sulu Terrane,Eastern Central China

The lithologies of the Chinese Continental Scientific Drilling main hole （CCSD-MH） are mainly comprised of orthogneiss,paragneiss,eclogite,amphibolite,and ultramafic rocks.The statistical results of logs of CCSD-MH indicate that ultramafic rocks are characterized by very high CNL （neutron log） and very low GR （gamma ray log） and RD （resistivity log）;eclogites are characterized by high DEN （density）,VP （P-wave velocity） and PE （photoelectric absorption capture cross section）;orthogneiss and paragneiss are characterized by high GR,U （uranium content）,Th （thorium content）,K （potassium content） and RD,and low DEN,PE,and CNL;logging values of amphibolite are between the logging values of eclogites and paragneiss.In addition,the logs could reflect the degree of retrograde metamorphism of eclogites.The upper section （100-2 000 m） shows higher DEN,PE,VP,and lower GR,U,Th,K,RD than the lower section （2 000-5 000 m）.Most logs of the upper section are more fluctuant than those of the lower section.This indicates that the upper section has more heterogeneities than the lower section.The cross plots of logs indicate that DEN,GR,K,and CNL are more powerful in identifying ultrahigh pressure metamorphic （UHPM） rocks at the CCSD-MH.GR value of the rocks from CCSD-MH shows obviously an increasing trend from ultramafic rock （the most mafic rocks at CCSD-MH） to orthogneiss （the most acid rocks at CCSD-MH）.On the contrary,DEN value decreases from the ultramafic rock to the orthogneiss.CNL log is a good indicator of the content of structure water in crystalline rocks.

Modeling of Borehole Radar for Well Logging Using Pseudo-spectral Time Domain Algorithm

In this article, numerical modeling of borehole radar for well logging in time domain is developed using pseudo-spectral time domain algorithm in axisymmetric cylindrical coordinate for proximate true formation model. The conductivity and relative permittivity logging curves are obtained from the data of borehole radar for well logging. Since the relative permittivity logging curve is not affected by salinity of formation water, borehole radar for well logging has obvious advantages as compared with conventional electrical logging. The borehole radar for well logging is a one-transmitter and two-receiver logging tool. The conductivity and relative permittivity logging curves are obtained successfully by measuring the amplitude radio and the time difference of pulse waveform from two receivers. The calculated conductivity and relative permittivity logging curves are close to the true value of surrounding formation, which tests the usability and reliability of borehole radar for well logging. The numerical modeling of borehole radar for well logging laid the important foundation for researching its logging tool.

Total organic carbon content logging prediction based on machine learning:A brief review

The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of oil or gas.Hence,accurately calculating the total organic carbon content in a formation is very important.Present research is focused on precisely calculating the total organic carbon content based on machine learning.At present,many machine learning methods,including backpropagation neural networks,support vector regression,random forests,extreme learning machines,and deep learning,are employed to evaluate the total organic carbon content.However,the principles and perspectives of various machine learning algorithms are quite different.This paper reviews the application of various machine learning algorithms to deal with total organic carbon content evaluation problems.Of various machine learning algorithms used for TOC content predication,two algorithms,the backpropagation neural network and support vector regression are the most commonly used,and the backpropagation neural network is sometimes combined with many other algorithms to achieve better results.Additionally,combining multiple algorithms or using deep learning to increase the number of network layers can further improve the total organic carbon content prediction.The prediction by backpropagation neural network may be better than that by support vector regression;nevertheless,using any type of machine learning algorithm improves the total organic carbon content prediction in a given research block.According to some published literature,the determination coefficient(R^(2))can be increased by up to 0.46 after using machine learning.Deep learning algorithms may be the next breakthrough direction that can significantly improve the prediction of the total organic carbon content.Evaluating the total organic carbon content based on machine learning is of great significance.

Research and experimental testing of a new kind electrokinetic logging tool

We designed a new downhole electrokinetic logging tool based on numericalsimulations and petrophysical experiments. Acoustic and electric receivers cannot be arrangedat the same depth, and the proposed composite electrokinetic logging tool offers a solutionto this problem. The sound field characteristics of the detectors were tested in a water tank inthe laboratory. Then, we calculated the sound pressure of the radiated acoustic field and thetransmitting voltage response of the transmitting transducers; in addition, we analyzed thedirectivity and application of the acoustic transmitting probe based on linear phased array.The results suggest that the sound pressure generated at 1500 mm spacing reaches up to 47.2k Pa and decreases with increasing acoustic source frequency. When the excitation signalsdelay time of adjacent acoustic transmitting subarrays increases, the radiation beam of themain lobe is deflected and its energy gradually increases, which presumably enhances theacoustoelectric conversion efficiency.

Study of sedimentary sequence cycles by well-seismic calibration

In order to solve the problems of the fine division of sedimentary sequence cycles and their change in two-dimensional space as well as lateral extension contrast, we developed a method of wavelet depth-frequency analysis. The single signal and composite signal of different Milankovitch cycles are obtained by numerical simulation. The simulated composite signal can be separated into single signals of a single frequency cycle. We also develop a well-seismic calibration insertion technology which helps to realize the calibration from the spectrum characteristics of a single well to the seismic profile. And then we determine the change and distribution characteristics of spectrum cycles in the two-dimensional space. It points out the direction in determining the variations of the regional sedimentary sequence cycles, underground strata structure and the contact relationship.

Identification of low-resistivity-low-contrast pay zones in the feature space with a multi-layer perceptron based on conventional well log data

In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated.In this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale volume.Moreover,different oil testing layers may have different causes of LRLC pay zones.As a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference data.The densitybased spatial clustering algorithm with noise(DBSCAN)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 dimensions.The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%.

Gas hydrate saturation model and equation for logging interpretation

Since gas hydrate exists in three different forms at the same time such as pore filling,particle support and separate stratification,the calculation method of hydrate saturation using traditional shaly sand formation interpretation models is equivalent to considering only the simple case that hydrate exists as pore filling,and does not consider other complex states.Based on the analysis of hydrate resistivity experimental data and the general form of the resistivity-oil(gas)saturation relationship,the best simplified formula of hydrate saturation calculation is derived,then the physical meaning of the three items are clarified:they respectively represent the resistivity index-saturation relationship when hydrate particles are completely distributed in the pores of formation rocks,supported in the form of particles,and exist in layers,corresponding quantitative evaluation method of hydrate saturation is built.The field application shows that the hydrate saturation calculated by this method is closer to that obtained by sampling analysis.At the same time,it also provides a logging analysis basis for the effective development after hydrate exploration.

Interest of Electrical Logging in Implementation of Technical Equipment for Water Drilling: Case of the Abrobakro Collecting Field (South-East Ivory Coast)

This work concerns the collecting field of the Abrobakro site, the objective of which is to determine the thickness of the layers crossed during drilling from electrical logging in order to propose the equipment plan for the various boreholes. The electrical logging data sheets, particularly those on resistivity and expeditious granulometry using a 1.25 mm and 2 mm mesh sieve, were used. The layer thicknesses are determined with the inflection points on the graphs. The electrical logging shows that the sands in the study area have resistivity values between 400 and 5000 Ω.m. The decrease in resistivity observed at 50 m for all boreholes shows that the static level of the groundwater is at this depth. The results of the accelerated granulometry show that the first 20 meters contain more fine particles and coarse to very coarse sands from 20 m. The granulometry of the screen laying areas shows that the 1.5 mm slot openings are best suited for all drilling in the Abrobakro collecting field. The diameter d10 of the aquiferous sands of the collecting field is close to 1.25 mm.

Research Progress on the Electrical Properties of Gas Hydrate-bearing Sediments

Electrical properties are important physical parameters of natural gas hydrate,and,specifically,resistivity has been widely used in the quantitative estimation of hydrate saturation.There are three main methods to study the electrical properties of gas hydrate-bearing sediments:experimental laboratory measurements,numerical simulation,and resistivity logging.Experimental measurements can be divided into three categories:normal electrical measurement,complex resistivity measurement,and electrical resistivity tomography.Experimental measurements show that the resistivity of hydrate-bearing sediment is affected by many factors,and its distribution as well as the hydrate saturation is not uniform;there is a distinct non-Archie phenomenon.The numerical method can simulate the resistivity of sediments by changing the hydrate occurrence state,saturation,distribution,etc.However,it needs to be combined with X-ray CT,nuclear magnetic resonance,and other imaging techniques to characterize the porous characteristics of the hydrate-bearing sediments.Resistivity well logging can easily identify hydrate layers based on their significantly higher resistivity than the background,but the field data of the hydrate layer also has a serious non-Archie phenomenon.Therefore,more experimental measurements and numerical simulation studies are needed to correct the parameters of Archie’s formula.