Log Interpretation Research Articles

Integrating machine learning and data analytics for geostatistical characterization of clastic reservoirs

An integrated multidisciplinary workflow of machine learning and data analytics was conducted for the multivariate geostatistical characterization of clastic reservoirs. This workflow was adopted for the estimation of lithofacies and permeability distributions in non-core intervals and wells on data from the upper shale member/Zubair formation in Luhais oil field, southern Iraq. Specifically, the workflow includes field and reservoir description, review of univariate, bivariate, and multivariate statistics for facies and petrophysical property distribution. In univariate and bivariate statistical analysis, the correction of core and log scale was adopted in order to have core permeability corrected to log scale. The discrete lithofacies distribution has been obtained for a few wells to be later incorporated in the permeability modeling and prediction.At first, the measured discrete lithofacies distributions were incorporated into classification algorithms to model the facies given the well logging data (shale volume, neutron porosity and water saturation) in order to predict the discrete facies distribution at other wells of missing records. The Multinomial Logistic Regression (Multinom), Logistic Boosting Regression (LogitBoost), and Extreme Gradient Boosting (XGBoost) were all comparatively adopted for lithofacies classification. After that, the resulting most accurate discrete facies distribution by LogitBoost were included along with the well logging interpretations into the multivariate permeability modeling through advanced machine learning approaches to model and predict the corrected core permeability given well logging interpretations for all wells in the reservoir. More specifically, the Multivariate Adaptive Regression Splines (MARS) and Smooth Generalized Additive Models (SGAM) were comparatively adopted to model and predict the core permeability at all wells. From this workflow, it was indicated that LogitBoost and MARS were the best machine learning algorithms to produce the most realistic lithofacies classification and permeability modeling, respectively.

Application of conventional logging interpretation fracture method based on neural network in offshore Oilfield L

The well data is required as a constraint for fracture prediction in the area. At present, imaging logging is considered to be the most accurate and effective means to interpret fractures, which can show the geological characteristics of the two-dimensional space of the borehole wall intuitively, vividly and clearly, but the experimental data is scarce due to the high cost of measurement. Conventional logging data are the main logging data in domestic oil and gas fields, so it is very important to use conventional logging data to effectively interpret fractures in the absence of drilling coring and imaging logging data. Based on the neural network algorithm, taking offshore Oilfield L in China as the application target, this paper optimizes the combination of conventional logging curves by establishing the nonlinear mapping relationship between the fracture strength curve and the conventional logging curve of sample wells with imaging logging data. By establishing the neural network model with nonlinear high learning ability for the optimized conventional logging curve and fracture strength curve, the fracture identification is carried out for the target interval of non sample wells. The results show that this method can combine geology, geophysics, mathematics, computer and so on, and can effectively identify fractures in the study area. It has good adaptability to offshore fractured reservoir.

Integrated characterization of ultradeep reef-shoal reservoir architecture: A case study of the Upper Permian Changxing Formation in the giant Yuanba gas field, Sichuan Basin, China

The Yuanba giant gas field in the Sichuan Basin produces from Upper Permian Changxing heterogeneous reef-shoal reservoirs. It is an ultradeep tight gas reservoir with burial depth of 7000 m. This paper proposes an integrated methodology incorporating the reservoir geology description, petrophysics characterization, seismic response analysis, forward seismic modeling, pseudo-acoustic inversion, and reservoir architecture imaging to identify the geologically complex reef-shoal reservoirs and predict the distribution of the producible reservoirs. The facies of reef cap, reef core, reef base and shoal are first identified based on observations and interpretations of cores, thin sections, mud logging, wireline logs, and formation microscanner images (FMIs). High-energy shoals, reef cores, and reef caps are the most favorable reservoir microfacies. Seismic responses of reef-shoal reservoirs are then investigated through forward modeling and well-seismic calibration. Four reef belts are identified based on seismic reflections and edge detection by seismic paleogeomorphologic features. Reservoir distribution and quantitative properties are predicted using the facies-controlled postattack seismic inversion technique through the correlation between reservoir properties and seismic attributes, which is aided by pseudo-gamma acoustic inversion to exclude the shale-bearing non-reservoir and pseudo-neutron acoustic inversion based on its good relationship with porosity. The reef reservoir thicknesses range from 10 m to 130 m, and the thickest reservoir is located in the northwestern Yuanba gas field. Finally, 3-D visualization is used to characterize the spatial variation of the interior architecture, connectivity, and distribution of a good reservoir in the reef system. The accuracy rate of prediction for reservoirs and non-reservoirs reaches 88% using this method. The technique has also been successfully used for the deployment of development wells and well trajectory optimization, and the rate of encountering the predicted reservoirs is 100%. This study provides a practical approach to identify and predict the tight carbonate gas reservoir with similar petrophysical responses of density and acoustic to non-reservoirs in the complex carbonate system.

A study on the application of ATEM in hydrogeological investigation

Airborne time-domain electromagnetic (ATEM) which is suitable for large-scale exploration in complex terrain areas is a high-efficiency, flexible geophysical investigation technique widely applied in various fields of work with broad application prospects. It is difficult to carry out ground geophysical exploration because of the great human disturbance in the study area. By applying ATEM in the air, together with ground geophysical investigation efforts, we were able to greatly improve the hydrogeological research degree of the area and provide useful geophysical data for the fundamental geological research there. The observation system used in our study was an AeroTEM-IV system. The survey data were converted into conductivity depth information, which was used together with existing geological data to examine the electrical structure of the area. By the hydrogeological logging interpretation result, we validated the reliability of the AEM data; comprehensive interpretation was also conducted using airborne magnetic (AM) data to improve the accuracy of the interpretation result. After converting the data across the area, we yielded a three-dimensional data body representing the electrical distribution in the area, with which we inferred the Quaternary formation thickness and aquifer distribution there. Our research and interpretation results confirm that ATEM can be used as a useful means for hydrogeological survey and fundamental geological investigation. It is an effective supplement to ground geophysical exploration.

Probabilistic logging lithology characterization with random forest probability estimation

Borehole lithology discrimination is the foundation of formation evaluation and reservoir characterization. Due to the limitation of costing or accuracy, direct discrimination methods such as borehole core and drilling cutting analysis are unable to be deployed to every well, while logging lithology interpretation provides an alternative solution for this. Recently, several machine learning algorithms such as the neural network, support vector machine, decision tree, and random forest have already been employed by researchers for automatic logging lithology interpretation. However, the vast majority of these studies belong to the category of deterministic lithology characterization. In this article, we propose a probability based fuzzy characterization method for more effective logging lithology interpretation. Moreover, to improve the accuracy of lithology probability estimation, we propose the probabilistic random forest algorithm and investigate its advantages referred to 8 existing probability estimation algorithms. Through the comparative experiments on 9 real-world logging lithology interpretation tasks, the feasibility and advantage of the proposed method are confirmed. Application case demonstrates that compared with traditional deterministic lithology characterization methods, probabilistic lithology characterization is able to provide more information about rhythm, heterogeneity, and formation properties, which worths further application and promotion to improve the fineness of formation evaluation and reservoir characterization.

Petrophysical Investigations of Shale Gas Formations in Poland

Results of laboratory experiments and well logging processing and interpretation are described to show the investigation trial realized on the Polish Silurian and Ordovician shale gas deposits in the Baltic Basin, north Poland. Standard and sophisticated laboratory methods of density, porosity, permeability, mineral composition and geochemical parameters measurements on rock samples were performed. Statistical methods were applied to standard, dipole sonic and geochemical well logs to limit the processed data without loss of useful petrophysical information, avoiding redundancy and doing the primary classification. The goal of this specific trial was to build petrophysical model of shale gas rocks taking into account low porosity, very low permeability, TOC equal at least 2 wt.% and organic matter as a kerogen and bitumen, free gas gathered in the rock matrix effective porosity and in secondary porosity. The unique model for the Polish shale gas formations is explained by age, depth of occurrence and mineral composition of the Silurian and Ordovician shales, different from those described in the literature.

Pore type identification in carbonate rocks using convolutional neural network based on acoustic logging data

Existing methods of well logging interpretation often contain uncertainties in the exploration and evaluation of carbonate reservoirs due to the complex pore types. Based on the time–frequency analysis of array acoustic logging data, the identification of pore types based on a convolutional neural network (CNN) was established. The continuous wavelet transform was first used to transform the 1-D acoustic wave data into 2-D time–frequency spectra as the input data of the CNN based on the pore aspect ratios. According to the acoustic logging data obtained from numerical simulations, a three-type (vug, interparticle-pores, and crack) prediction was established to validate the identification method. The noise-sensitivity analyses demonstrate that our method is stable for noise mixed signals. The CNN-based identification method was used to analyze the field acoustic logging data in a carbonate reservoir. According to the description of the pore structures from the core analysis, the formation pores were divided into two types (cracks and interparticle-pores). The accuracy of the method using field acoustic logging data can reach 90%. This work provides promising means for pore type identification from complex acoustic logging data by applying deep learning technologies, which can be easily extended into other similar neighboring carbonate reservoirs.

Уточнение значений проницаемости при адаптации гидродинамической модели

Reservoir simulation models are used to design oil field developments, estimate efficiency of geological and engineering operations and perform prediction calculations of long-term development performances. A method has been developed to adjust the permeability cube values during reservoir model history-matching subject to the corederived dependence between rock petrophysical properties. The method was implemented using an example of the Bobrikovian formation (terrigenous reservoir) deposit of a field in the Solikamskian depression. A statistical analysis of the Bobrikovian formation porosity and permeability properties was conducted following the well logging results interpretation and reservoir modelling data. We analysed differences between the initial permeability obtained after upscaling the geological model and permeability obtained after the reservoir model history-matching. The analysis revealed divergences between the statistical characteristics of the permeability values based on the well logging data interpretation and the reservoir model, as well as substantial differences between the adjusted and initial permeability cubes. It was established that the initial permeability was significantly modified by manual adjustments in the process of history-matching. Extreme permeability values were defined and corrected based on the core-derived petrophysical dependence KPR = f(KP) , subject to ranges of porosity and permeability ratios. By using the modified permeability cube, calculations were performed to reproduce the formation production history. According to the calculation results, we achieved convergence with the actual data, while deviations were in line with the accuracy requirements to the model history-matching. Thus, this method of the permeability cube adjustment following the manual history-matching will save from the gross overestimation or underestimation of permeability in reservoir model cells.

Research on triaxial array induction logging response in inclined anisotropic formation

The logging response calculation and fast inversion are the key and difficulties for logging data interpretation and formation parameter estimation of the triaxial array induction logging. We transform the formation conductivity from media coordinate to instrument coordinate based on coordinate transformation theory. This paper examines anisotropic response of the triaxial array induction in inclined anisotropic formation by finite element method. The greater the anisotropy coefficient is, the more obvious the curve separation. The apparent conductivity decreases with the increase of dip angle for the coaxial system, but the coplanar system increases. The research results show that the apparent conductivity of the coplanar system appears as obviously positive and negative horns on the horizontal interfaces, which is generated by the accumulated surface charge at the boundary of the layer.

Integration of Formation Evaluation Data Overcomes Offshore Coring Challenges

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 195866, “Overcoming Coring Challenges in a New Unconventional Play Offshore by Integration of Formation Evaluation Data,” by Thomas Bradley, SPE, Baker Hughes, and Per Henrik Fjeld and Jonathan Scott, Aker BP, et al., prepared for the 2019 SPE Annual Technical Conference and Exhibition, Calgary, 30 September-2 October. The paper has not been peer reviewed. A well was drilled into a prospective unconventional mudstone play offshore Norway. Two of five coring runs were successful while the rest yielded little to no core recovery. Subsequent investigation of the core substantiated that the coring issues largely had natural causes. This understanding is being applied to two imminent coring operations and has driven selection of drilling, coring, and wireline technology and procedures and is informing casing design. Introduction The Valhall field is in Block 2/8 of the Norwegian North Sea. The primary reservoir is Cretaceous chalk, located at approximately 2400 m true vertical depth. Production of this reservoir began in 1982. Above this reservoir are other formations, including a shallower potential unconventional mudstone reservoir. A production well was drilled targeting the Cretaceous chalk reservoir. As part of the drilling program, logging-while-drilling (LWD) and wireline logs - including LWD density, neutron, gamma ray and acoustic, wireline spectral gamma ray, cross-dipole acoustic, nuclear-magnetic-resonance, and formation-testing and -sampling - were run across the potential mudstone reservoir to appraise the standard petrophysical properties. A conventional core was also attempted; however, recovery was poor, with repeated jams. Coring Challenges During coring operations, significant problems were encountered when coring the mudstone intervals. These challenges are illustrated in Fig. 1, which shows a section of the core from xx14 m, demonstrating the typical condition of the recovered core when still in the core barrel. At the time of operation, poor recovery was attributed to the coring practices used and no further coring was attempted. Formation Evaluation The mudstone interval was not a primary target for the well. Therefore, limited formation evaluation of these intervals was performed, mainly to estimate the petrophysical properties. However, at the time of initial logging, more-advanced formation evaluation data (e.g., images, full waveform acoustic, and nuclear-magnetic-resonance data) were not evaluated fully. A few years after initial operations, the log data were revisited - specifically, detailed interpretation was conducted for all recorded log measurements. On examination of the results over much of the interval, the integration of the conventional and advanced volumetric measurements gave considerable extra value in assessing the petrophysical properties of the mudstone. However, detailed discussion of these results is outside the scope of this paper. In addition to the extra information gleaned from the advanced volumetric interpretation, over certain intervals, some unusual log responses and interpretation results were seen that were deemed worthy of further investigation.

Research on well logging evaluation method of igneous reservoir in Nanpu No.5 structure

ABSTRACT Igneous gas reservoir in Nanpu No. 5 structure which located in the east coast of China is one of the important areas for deep natural gas exploration. Compared with conventional sandstone reservoirs, the well logging evaluation of igneous reservoirs is more complex and difficult. This research provides a practical workflow for log interpretation of igneous reservoir, including curves reconstruction, reservoir parameters settlement, and fluid identification. The target reservoirs (basalt and basaltic tuff reservoirs) are identified through the lithology identification chart, and then the logging data of the borehole enlargement intervals are reconstructed by BP neural network, the average absolute error between reconstructed data and core experimental data is 0.021 g/cm3 and the reservoir parameters are calculated based on the reconstructed curves and ECS logging data. Finally, the time-frequency analysis method is used to identify the gas and water layers. Through the above method, we solve some difficult problems of logging interpretation in igneous reservoir. It could provide a very practical method for well logging evaluation of igneous reservoirs.

On the depth of detection of logging-while-drilling resistivity measurements for looking-around and looking-ahead applications

The depth of detection (DOD), which is an important concept in logging data interpretation, describes the detection capability of the borehole measurements. We have extended the definition of DOD for azimuthal information, namely, the geosignal delivered by azimuthal resistivity tools, to resistivity logs in logging-while-drilling (LWD) applications. Instead of using the radial geometric factor, the detection thresholds in predicting a geologic boundary are used to describe the DOD of a measurement. This definition unifies the criteria to evaluate the detectability of different borehole measurements, such as LWD resistivity measurements and geosignals. It also can be generalized to other kinds of well logging methods in LWD applications. Using the proposed definition, we analyze the detection capability of the LWD resistivity measurements in looking-around and looking-ahead applications; they provide more tangible descriptions. In vertical or near-vertical wells, the definition provides an indicator to evaluate the capability and reliability of looking ahead of deep/ultradeep LWD resistivity tools. The investigations on the influence of the DOD on the distance-to-boundary inversion, which can help in developing a robust and accurate inversion scheme, also are presented and discussed.

Semi-supervised learning for lithology identification using Laplacian support vector machine

Lithology identification is a fundamental task in well log interpretation. Considering the presence of substantial unlabeled data in the field of petroleum exploration, this paper investigates the semi-supervised learning method for lithology identification, and proposes a semi-supervised lithology identification workflow. In the workflow, the Laplacian support vector machine is employed to achieve semi-supervised learning. The feature similarity and depth similarity are introduced to reveal the data distribution characteristic, thus enabling us to elevate the classification performance of the Laplacian support vector machine dealing with the issue of lacking labels. Considering the well logging dataset without label, K-means clustering is used to select the k well logging samples that should be labeled by experts, based on which Laplacian support vector machine algorithm can be then conducted. The proposed method is compared with the supervised method (e.g., support vector machine) that excludes the unlabeled data and the semi-supervised method without considering the depth similarity. The comparison experiments are conducted on four datasets that are collected from the Jiyang Depression, Bohai Bay Basin, China. The experimental results show that the utilization of unlabeled data can improve identification performance, especially that of minority lithology classes. It is also verified that the information provided by feature similarity and depth similarity are helpful for lithology identification.

Automatic interpretation of cement evaluation logs from cased boreholes using supervised deep neural networks

The integrity of cement in cased boreholes is typically evaluated using well logging. However, well logging results are complex and can be ambiguous, and decisions associated with significant risks may be taken based on their interpretation. Cement evaluation logs must therefore be interpreted by trained professionals. To aid these interpreters, we propose a system for automatically interpreting cement evaluation logs, which they can use as a basis for their own interpretation. This system is based on deep convolutional neural networks, which we train in a supervised manner using a dataset of around 60 km of interpreted well log data. Thus, the networks learn the connections between data and interpretations during training. More specifically, the task of the networks is to classify the bond quality (among 6 ordinal classes) and the hydraulic isolation (2 classes) in each 1m depth segment of each well based on the surrounding 13 m of well log data. We quantify the networks' performance by comparing over all segments how well the networks' interpretations of unseen data match the reference interpretations. For bond quality, the networks’ interpretation exactly matches the reference 51.6% of the time and is off by no more than one class 88.5% of the time. For hydraulic isolation, the interpretations match the reference 86.7% of the time. For comparison, a random-guess baseline gives matches of 16.7%, 44.4%, and 50%, respectively. We also compare with how well human reinterpretations of the log data match the reference interpretations, finding that the networks match the reference somewhat better. This may be linked to the networks learning and sharing the biases of the team behind the reference interpretations. An analysis of the results indicates that the subjectivity inherent in the interpretation process (and thereby in the reference interpretations we used for training and testing) is the main reason why we were not able to achieve an even better match between the networks and the reference.

Identifying the lithology of volcanic rocks by using the time-frequency features of array acoustic logging data

The traditional acoustic logging signal processing method is computing the slowness of each component wave by time-domain or frequency-domain methods. But both of the two methods are limited. To combine the signals’ times, frequencies, or amplitudes, we have analyzed the array acoustic logging signals by the fractional Fourier transform and the Choi-Williams distribution. First, we apply the fractional Fourier transform on an array acoustic logging waveform with proper [Formula: see text], then the Choi-Williams distribution analysis method is used to process the signal in the fractional Fourier domain, and finally the result will show in the fractional Fourier time-frequency domain. The results show the following. The array acoustic logging signal is received earlier in the mudstone and diabase formation than in the tuff and breccia formations. The basic frequencies of the compressional wave (P-wave) are not very different, but the basic frequency of the shear wave (S-wave) is highest in the tuff formation and is lowest in the diabase formation. The relative energies of each component wave in the diabase, mudstone, tuff, and breccia formation can be summarized as: for the P-wave, diabase > mudstone ≈ tuff ≈ breccia; for the S-wave, diabase ≈ mudstone > breccia > tuff; and for the Stoneley wave, diabase > mudstone > tuff > breccia. The signal processing method combining the fractional Fourier transform and the Choi-Williams distribution can comprehensively research the time, frequency, and amplitude, thereby improving the segmentation of the time and frequency domains and providing a new method for interpretation of array acoustic logging.

Hartha Formation divisions Based on Well Logs Analysis in Majnoon Oil Field, Southern Iraq

This study aims to evaluate reservoir characteristics of Hartha Formation in Majnoon oil field based on well logs data for three wells (Mj-1, Mj-3 and Mj-11). Log interpretation was carried out by using a full set of logs to calculate main petrophysical properties such as effective porosity and water saturation, as well as to find the volume of shale. The evaluation of the formation included computer processes interpretation (CPI) using Interactive Petrophysics (IP) software. Based on the results of CPI, Hartha Formation is divided into five reservoir units (A1, A2, A3, B1, B2), deposited in a ramp setting. Facies associations is added to well logs interpretation of Hartha Formation, and was inferred by a microfacies analysis of thin sections from core and cutting samples. The CPI shows that the A2 is the main oil- bearing unit, which is characterized by good reservoir properties, as indicated by high effective porosity, low water saturation, and low shale volume. Less important units include A1 and A3, because they have low petrophysical properties compared to the unit A2.

Petrophysical analysis of well logs data for identification and characterization of the main reservoir of Al Baraka Oil Field, Komombo Basin, Upper Egypt

The current study presents the log analysis results from two vertical onshore wells in Al Baraka oil Field, Komombo basin, upper Egypt. The Geophysical logs comprising gamma ray (GR), caliper (Cali), resistivity (LLD, LLS, and MSFL), photoelectric effect (PEE), neutron (NHPH) and density (RHOB) are used to study the petrophysical characteristics of the main identified reservoirs in this field. The key purpose of well logging analysis and interpretation is to obtain petrophysical properties of reservoirs such as shale volume, porosity, hydrocarbon saturation, net pay thickness, etc., for hydrocarbon exploration. Petrophysical analysis of well logs reveals that Al Baraka wells consist of three types of lithology: sand, shale, and siltstone. The obtained results show that there are two hydrocarbon-bearing zones named (S-E) and (S-D) zone for Al Baraka-4 well and Al Baraka-14 well, respectively; both had hydrocarbon reservoir bearing oil and lie in Six Hills Fm of the Lower Cretaceous. The generated cross-plots showed that the main reservoir lithology is composed of shaley sandstone to sandstone. The computed petrophysical parameters for the identified and characterized reservoir layers reveal that they have a total porosity range of 18.2–20.1%, water saturation of 57.7–36.6%, shale volume of 20–8.5% for (S-E) and (S-D) zone, respectively. The hydrocarbon saturation reaches to 42.5% for (S-E) zone and 36.6% for (S-E) zone; both hydrocarbons in these zones are movable. The net reservoir pay thickness is high in (S-E) zone reach to 24 ft. Below and above the oil-bearing reservoirs, there is a thick shale bed which acts as potential source rock or as a seal rock. The overall results indicated that the sandy reservoir units of ‘Al Baraka’ Field have the potential to contain significant accumulations of hydrocarbon, essential oil. It is recommended to integrate more wells and seismic data to better evaluate the reservoirs.

An Integrated quantitative modeling approach for fault-related fractures in tight sandstone reservoirs

Fault-related fractures widely exist in tight sandstone reservoirs, which greatly improve the heterogeneity of reservoir. Unraveling the spatial distribution of fractures is most important for enhancing oil and gas recovery. In this study, an integrated quantitative modeling approach is proposed and applied to the Boxing sag in Jiyang Depression, Bohai Basin, China. From observations of drilled cores and well log interpretations we construct the internal structure of the fault damage zone and establish its geological conceptual model. A single well rock mechanics model is built by applying BP neural network method on rock mechanics experimental data. With the sedimentary facies guided lithofacies model, a three-dimensional heterogeneous rock mechanics model is constructed by integrating three geostatistical methods (i.e. sequential Gaussian simulation method, probability volume modeling method and trend modeling method). This workflow assigns the geological concept and rock mechanics characteristics to the geological volume. Based on the energy conservation principle, the relationship between fracture density and strain energy is derived using different fracture criteria. Based on simulation and calculation results, the fracture density model is established by deterministic modeling. Finally, these properties are incorporated into a DFN model. The model results show that, at the regions near the faults, the fracture development is mainly controlled by faulting. The induced fracture zone is a fracture-intensive area. However, there are few fractures within the fault core zone. The regions far from the faults are mainly controlled by the rock mechanics properties of geological materials, which show zonal characteristics. This model is validated by data from five wells in this region, which shows a strong correlation between predicted fracture density and water cut in produced fluid.

Sequence stratigraphic investigation of the Early Miocene Formations in the Zagros Folded belt using wireline well logs analysis

Based on the available core sample description and wireline log analysis, sedimentary facies, microfacies and lithofacies interpretation are evaluated to determine the large scale architecture in the Early Miocene formations, from close to the contemporaneous Lower Miocene depositional margin of the Zagros basin, as represented in outcrop sections, to productive oil field within the Kirkuk embayment equivalents ca. 100 km to the south-west and west. Sequence stratigraphic methods of sedimentary log interpretation and log correlation are used to construct a model of the overall regressive and transgressive or combined transgressive to regressive character of each of the formations, thus providing an indication of the gross reservoir stacking patterns over this distance. Based on outcrop descriptions and wireline log data the Dhiban Formation is interpreted as a lowstand system tract (LST) of a third order depositional sequence, lying between the Euphrates Formation regressive parasequence set that could represent a highstand systems tract (HST) of a third order sequence and the transgressive parasequence set (third order transgressive systems tract, TST) of the lower part of the Jeribe Formation. Several higher order transgressive and regressive cycles are evident within the Dhiban Formation in the subsurface sections. The transgressive cycles within the Dhiban formation are associated with the carbonate layers. Yellowish grey, friable anhydrite is recognized in the Dhiban Formation at outcrop, and thick anhydrite of low gamma ray and high density readings was identified in the subsurface sections. This lowstand evaporite system filling the basin centre can be confirmed as the relevant model for the mixed carbonate-evaporite basin at the scale of a third order cycle.

Quantitative pore‐type characterization from well logs based on the seismic petrophysics in a carbonate reservoir

ABSTRACTQuality, availability and consistency of the measured and interpreted well log data are essential in the seismic reservoir characterization methods, and seismic petrophysics is the recommended workflow to achieve data consistency between logs and seismic domains. This paper uses seismic petrophysics workflow to improve well logs and pore geometry interpretations for an oil carbonate reservoir in the Fahliyan Formation in the southwest of Iran. The petrophysical interpreted well logs, rock physics and well‐to‐seismic tie analysis are integrated into the proposed workflow. Our implementation incorporates revising petrophysical well log interpretations and updating pore geometry characteristics to obtain a better well‐tie quality. We first propose an improved pore‐type characterization approach based on both P‐ and S‐wave velocities for quantifying pore geometry. Then, seismic logs are estimated accordingly, and the results are used in the well‐to‐seismic analysis. The quality of the well‐tie is improved, furthermore, by iterating on the petrophysical interpreted well logs as well as the calculated pore geometries. For the intervals with high‐quality data, our workflow improves the consistency between the results of measured and modelled seismic logs. For the intervals with problematic well logs, the application of our proposed workflow results in the successful replacement of the poor data and subsequently leads to an improved wavelet estimation and well‐tie results. In both cases, a higher quantification of pore geometries is achieved, which in turn is confirmed by the core images and formation micro‐imager analysis.