Porosity Logs Research Articles

New vuggy porosity models-based interpretation methodology for reliable pore system characterization, Ordovician carbonate reservoirs in Tahe Oilfield, North Tarim Basin

Carbonate rocks are in general complex and commonly contain compound pore systems that can significantly affect the variations of physical properties; their petrophysical evaluation remains challenging, and the results are often uncertain.This paper presents a new approach developed to establish vuggy porosity models that can be applied to successfully estimate vuggy porosity, both connected and separate vug fraction. It involves mainly partitioning the total porosity into fractions of its basic components, such as fractures, matrix porosity, and vugs. To estimate and decompose the total porosity, wireline methods involving standard porosity and borehole electrical image logs are integrated based on rock-physics modeling and image analysis techniques through a series of steps, which specifically have particular set of procedures performed rigorously to obtain accurate results. The determined fractions of the total-vug and separate-vug porosities are related to acoustic parameters to establish total-vug and separate-vug porosity models, respectively. Core analysis and thin-section-derived optical porosity (PHIO) are used to verify the log-derived porosities and validate the newly-established total porosity partition procedure.The newly proposed vuggy porosity models yield optimal estimates that significantly match the measured quantities. They also show statistically better prediction accuracy, compared with previous models in literature, especially typical Lucia vug porosity models. In addition, the results indicate that the density log, which its combination to sonic and resistivity logs can enable separate-vug porosity calculation, is a good-total porosity estimator at least in low-touching vug zones. Together, this has proven the effectiveness of the present approach for porosity partitioning in carbonate rocks, and the vuggy porosity models derived could therefore be applied to other similar carbonate reservoir studies.

Lower Cretaceous Rodby and Palaeocene Lista Shales: Characterisation and Comparison of Top-Seal Mudstones at Two Planned CCS Sites, Offshore UK

Petroleum-rich basins at a mature stage of exploration and production offer many opportunities for large-scale Carbon Capture and Storage (CCS) since oil and gas were demonstrably contained by low-permeability top-sealing rocks, such as shales. For CCS to work, there must be effectively no leakage from the injection site, so the nature of the top-seal is an important aspect for consideration when appraising prospective CCS opportunities. The Lower Cretaceous Rodby Shale and the Palaeocene Lista Shale have acted as seals to oil and gas accumulations (e.g., the Atlantic and Balmoral fields) and may now play a critical role in sealing the Acorn and East Mey subsurface carbon storage sites. The characteristics of these important shales have been little addressed in the hydrocarbon extraction phase, with an understandable focus on reservoir properties and their influence on resource recovery rates. Here, we assess the characteristics of the Rodby and Lista Shales using wireline logs, geomechanical tests, special core analysis (mercury intrusion) and mineralogical and petrographic techniques, with the aim of highlighting key properties that identify them as suitable top-seals. The two shales, defined using the relative gamma log values (or Vshale), have similar mean pore throat radius (approximately 18 nm), splitting tensile strength (approximately 2.5 MPa) and anisotropic values of splitting tensile strength, but they display significant differences in terms of wireline log character, porosity and mineralogy. The Lower Cretaceous Rodby Shale has a mean porosity of approximately 14 %, a mean permeability of 263 nD (2.58 × 10−19 m2), and is calcite rich and has clay minerals that are relatively rich in non-radioactive phases such as kaolinite. The Palaeocene Lista Shale has a mean porosity of approximately 16% a mean permeability of 225 nD (2.21 × 10−19 m2), and is calcite free, but contains abundant quartz silt and is dominated by smectite. The 2% difference in porosity does not seem to equate to a significant difference in permeability. Elastic properties derived from wireline log data show that Young’s modulus, material stiffness, is very low (5 GPa) for the most shale (clay mineral)-rich Rodby intervals, with Young’s modulus increasing as shale content decreases and as cementation (e.g., calcite) increases. Our work has shown that Young’s modulus, which can be used to inform the likeliness of tensile failure, may be predictable based on routine gamma, density and compressive sonic logs in the majority of wells where the less common shear logs were not collected. The predictability of Young’s modulus from routine well log data could form a valuable element of CCS-site top-seal appraisals. This study has shown that the Rodby and Lista Shales represent good top-seals to the Acorn and East Mey CCS sites and they can hold CO2 column heights of approximately 380 m. The calcite-rich Rodby Shale may be susceptible to localised carbonate dissolution and increasing porosity and permeability but decreasing tendency to develop fracture permeability in the presence of injected CO2, as brittle calcite dissolves. In contrast, the calcite-free, locally quartz-rich, Lista Shale will be geochemically inert to injected CO2 but retain its innate tendency to develop fracture permeability (where quartz rich) in the presence of injected CO2.

A data-driven approach to predict compressional and shear wave velocities in reservoir rocks

Compressional and shear wave velocities (Vp and Vs respectively) are essential reservoir parameters that can be used to delineate lithology, calculate porosity, identify reservoir fluids, evaluate fracture and calculate mechanical properties of rocks. In this study, the potential application of intelligent systems in predicting Vp and Vs of reservoir rocks is presented. To date, considerable efforts are being carried out to obtain the best set of parameters capable of predicting Vp and Vs with a high degree of accuracy. Three intelligent models namely artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and least square support vector machine (LSSVM) were used in this study. The different models were based on the available information sourced from wireline log data. Parametric studies showed that measured depth, neutron porosity, gamma-ray, and density log data are vital in predicting both Vp and Vs. In developing the models, a comprehensive dataset available from one of the oil fields in the Norwegian North Basin was used. In evaluating the different models, two different statistical parameters namely Pearson’s correlation coefficient (R2) and root mean square error (RMSE) were considered. It was found that the LSSVM model is the most accurate technique for predicting both Vp and Vs. LSSVM model predicted the Vp with R2 and RSME of 0.9706 and 0.0893 respectively. In addition, the model showed an excellent accuracy level in the prediction of Vs with R2 and RMSE of 0.9991 and 0.0457 respectively. The proposed approach, if implemented, is crucial for geoscientists, reservoir and drilling engineers working on reservoir characterization and drilling operations.

A new approach to heterogeneity analysis in a highly complex carbonate reservoir by using borehole image and conventional log data

This study outstretches a new method to specify the heterogeneity of the studied reservoir by combining BHI (Borehole Image Log) and conventional log data for 4 existing wells. The first step to achieve this goal was utilizing borehole electrical images to assess the quantity of vugs/modals fraction and porosity distribution. Spectrum porosity was calculated through probabilistic analysis based on core and FMI data which has specified two general types of porosity in studied field as primary porosity (microporosities and intercrystalline) and secondary porosity (vugs). Due to irregular dispersion, non-categorization, and scattering of the porosity–permeability graph obtained from laboratory core data, next step was using petrophysical image facies prediction for generating an electrical facies to incorporate the different reservoir quality. This lithology (litho types) is produced by BHI analysis which reflects geological and petrophysical properties of the field. Then a porosity–permeability cross-plot has been made based on core data and produced facies codes which build in the previous step to check the validity of BHI Facies code extraction. Finally, heterogeneity analysis has been done through an innovative step-by-step workflow to determine spectrum porosity log which is divided into 6 categories/portions as Resistive, Matrix, Isolated, Connected, Bed boundaries and Fractures porosity.

Physical parameter modeling of carbonate dissolution-vug reservoirs based on porosity inversion with optimization algorithm

Building a 3D porosity model for the carbonate reservoir is crucial for the planning of hydrocarbon development. However, properly estimating the porosity of carbonate reservoir using wireline logs is still a challenge task due to the complexity of vugs. We treat the porosity estimate of carbonate as an optimization problem and propose to estimate the porosity of carbonate reservoir by integrating several kinds of petrophysics data. We begin with establishing a series of linear equations between the porosity and wireline logs. The objective is computing a porosity value that simultaneously minimizes the defined linear equations. The multi-functions minimization can successfully improve the robust of the proposed method to noise and the “small” wireline log values due to the complex dissolution vug reservoirs. We build 3D porosity model by applying the cooperative kriging stochastic simulation algorithm to the porosity estimated at well bore location and inverted P-impedance from seismic data. We demonstrate the proposed workflow by applying it to the Ordovician carbonate dissolution-vug reservoir in the L area of Tahe oilfield. The statistical analysis shows that the built 3D model has a high correlation with the dynamic production data of the reservoir.

Reservoir characterization and static earth model for potential carbon dioxide storage in Upper Pennsylvanian cyclothems, Nebraska, United States

This study estimates the carbon storage potential of interbedded shales and carbonate rocks (cyclothems) in the Pennsylvanian Lansing and Kansas City groups (LKC) on the Cambridge arch in southwestern Nebraska. This effort is essential to the development of a CO2 storage strategy for the Integrated Midcontinent Stacked Carbon Storage Hub project as part of the Department of Energy–National Energy Technology Laboratory’s Carbon Storage Assurance Facility Enterprise initiative. We present a static earth (SE) model representing the 250-ft (76-m)-thick LKCs. This model is based on vintage (mostly pre-1970) well logs from the Sleepy Hollow field (Red Willow County, Nebraska) as well as a new (June 2019) stratigraphic test well drilled expressly for the purpose of the present study. Interpretations of advanced petrophysical logs and cores from this new well were crucial ingredients in the development of the geologic framework for SE model development. Gamma-ray (GR) logs readily differentiate carbonate and mudstone units within the LKC, allowing the differentiation of three GR facies for use in a facies model. Carbonate rock units, which are composed of multiple textures, were correlated across the field. We capture the heterogeneity of these carbonates during petrophysical modeling using effective porosity logs along with Gaussian random function simulation conditioned by the three-dimensional facies model. The SE model was used in computing carbon storage estimates for each LKC carbonate zone over an area of 1 mi2. In total, supercritical CO2 storage is estimated at 602,157 t/mi2 (232,494 t/km2) when using a deterministic saline storage efficiency factor of 0.1.

Missing well log prediction using convolutional long short-term memory network

Reservoir characterization involves integration of different types of data to understand the subsurface rock properties. To incorporate multiple well log types into reservoir studies, estimating missing logs is an essential step. We have developed a method to estimate missing well logs by using a bidirectional convolutional long short-term memory (bidirectional ConvLSTM) cascaded with fully connected neural networks. We train the model on 177 wells from mature areas of the UK continental shelf (UKCS). We test the trained model on one blind well from UKCS, three wells from the Volve field in the Norwegian continental shelf, one well from the Penobscot field in the Scotian shelf offshore Canada, and one well from the Teapot Dome data set in Wyoming. The method takes into account the depth trend and the local shape of logs by using ConvLSTM architecture. The method is examined on sonic log prediction and can produce an accurate prediction of sonic logs from gamma-ray, density, and neutron porosity logs. The advantages of our method are that it is not applied on an interval by interval basis like rock-physics-based methods and it also outputs the uncertainties facilitated by dropout layers and Monte Carlo sampling at inference time.

A new empirical model for enhancing well log permeability prediction, using nonlinear regression method: Case study from Hassi-Berkine oil field reservoir – Algeria

The reservoir permeability (K) factor is the key parameter for reservoir characterization. This parameter is considered as a determinant reservoir quality index. Depending on the data required and procedure availability, permeability can be defined from several methods such as; well test interpretation, wireline formation tester, and core data. These approaches can also be in assumption with permeability prediction targeting the non-cored sections. According to a similar status, well logs records can be an interesting support tool in use to reach the planned objectives. Thus, this investigation consists of finding out a model able to estimate the well log permeability and adjusting the outcome to the core permeability results.In this led research, the applied approach to the core data, to start with, was aimed to determine the reservoir rock types (RRT) using the flow zone indicator (FZI) method. The obtained classification allows stating a permeability model for each rock type.In order to calculate permeability from well logs, FZI has been founded out. A multi-regression technique was used to analyze the relationship of FZI with respect to specific logs such as Gamma-ray (GR), Density Log (RHOB), and Sonic log (DT). An objective function has been designated to minimize the quadratic error between the observed normalized FZI coming from core data, and the normalized FZI calculated from well logs. This process is carried out to identify a mathematical correlation allowing the estimation of FZI from porosity logs, leading to permeability determination. As results, permeability from logs was supporting relatively permeability defined from cores. The final results can be an accurate and real test for associating the exactitude performance of logging data records in boreholes with respect to the overall reservoir characterization sections. Thus, the applied investigation can be a genuine and quick method for essentially a specific deduction regarding the non-cored reservoir sections, with reference to rock typing, permeability and probably further reservoir factors.

Petrophysical Analysis of an Iraqi Gas Field (Mansuriya Gas Field)

Mansuriya Gas field is an elongated anticlinal structure aligned from NW to SE, about 25 km long and 5-6 km wide. Jeribe formation is considered the main reservoir where it contains condensate fluid and has a uniform thickness of about 60 m. The reservoir is significantly over-pressured, (TPOC, 2014).
 This research is about well logs analysis, which involves the determination of Archie petrophysical parameters, water saturation, porosity, permeability and lithology. The interpretations and cross plots are done using Interactive Petrophysics (IP) V3.5 software.
 The rock parameters (a, m and n) values are important in determining the water saturation where (m) can be calculated by plotting the porosity from core and the formation factor from core on logarithmic scale for both and the slope which represent (m) then Pickett plot method is used to determine the other parameters after calculating Rw from water analysis .
 The Matrix Identification (MID), M-N and Density-Neutron crossplots indicates that the lithology of Jeribe Formation consists of dolomite, limestone with some anhydrite also gas-trend is clear in the Jeribe Formation.
 The main reservoir, Jeribe Formation carbonate, is subdivided into 8 zones namely J1 to J8, based mainly on porosity log (RHOB and NPHI) trend, DT trend and saturation trend.
 Jeribe formation was considered to be clean in terms of shale content .The higher gamma ray because of the uranium component which is often associated with dolomitisationl and when it is removed and only comprises the thorium and potassium-40 contributions, showed the gamma response to be low compared to the total gamma ray response that also contains the uranium contribution.While the Jeribe formation is considered to be clean in terms of shale content so the total porosity is equal to the effective porosity.No porosity cut off is found if cutoff permeability 0.01 md is applied while the porosity cut off approximately equal to 0.1 only for unit J6 & J8 if cutoff permeability 0.1 md is applied .
 It can be concluded that no saturation cutoff for the units of Jeribe formation is found after a cross plot between water saturation and log porosity for the reservoir units of Jeribe formation and applied the calculated cut off porosity.
 The permeability has been predicted using two methods: FZI and Classical, the two methods yield approximately the same results for all wells.

Case study: Seismic resolution and reservoir characterization of thin sands using multiattribute analysis and bandwidth extension in the Daqing field, China

The Daqing field, located in the Songliao Basin in northeastern China, is the largest oil field in China. Most production in the Daqing field comes from seismically thin sand bodies with thicknesses between 1 and 15 m. Thus, it is not usually possible to resolve Daqing reservoirs using only conventional seismic data. We have evaluated the effectiveness of seismic multiattribute analysis of bandwidth extended data in resolving and making inferences about these thin layers. Multiattribute analysis uses statistical methods or neural networks to find relationships between well data and seismic attributes to predict some physical property of the earth. This multiattribute analysis was applied separately to conventional seismic data and seismic data that were spectrally broadened using sparse-layer inversion because this inversion method usually increases the vertical resolution of the seismic. Porosity volumes were generated using target porosity logs and conventional seismic attributes, and isofrequency volumes were obtained by spectral decomposition. The resulting resolution, statistical significance, and accuracy in the determination of layer properties were higher for the predictions made using the spectrally broadened volume.

ANALISIS RESERVOAR MIGAS (SANDSTONE) MENGGUNAKAN MULTIATRIBUT SEISMIK PADA LAPANGAN TG12, CEKUNGAN BARITO, KALIMANTAN SELATAN

The study using multi attribute seismic has been done on TG12 field which situated at Lower Foreland Formation, Barito Basin dominated by sandstone on layer area of the target X. The objective of the study is to map the sandstone reservoir by predict distribution value of gamma ray log, neutron porosity, and density which goes through wells such as FM1, FM2, FM3, and FM4 on seismic data. Total attribute that is being used by step wise regression method by considering validation error. Multiattribute process only applied on FM2, FM3, and FM4 wells, whereas FM1 is used as a test well to determine the correlation value between seismic data and log data that is being used. In addition, from well test correlation showing great correlation result of neutron porosity log and density log both obtain the correlation around 0.6322 and 0.6557 while the gamma ray log obtain low correlation that is 0.1647 towards multi attribute result. The processing result of multi attribute obtained distribution of sandstone with gamma ray estimation range value of 65-75.8API, neutron porosity estimation range value 0.15-0.2262, while density estimation range value 2.4308-2.77gr/cc.

Reservoir Characterization of the Early Jurassic Butmah Formation Using Well Log Data in Selected Wells from Iraqi Kurdistan Region

The reservoir characterization of Butmah Formation (Early Jurassic) was studied in two wells: (AT-1) and (SH-1B) from Atrush and Shaikhan Oilfields respectively, in Iraqi Kurdistan Region. Well logs including; sonic, density, neutron, gamma ray, and resistivity, were employed to investigate the formation. The petrophysical properties, such as lithology, shale volume, porosity, water saturation, hydrocarbon saturation, and bulk water volume, were estimated using Interactive Petrophysics Software (IP). Water saturation, bulk volume of water, residual hydrocarbon, and movable hydrocarbon were plotted as Computer Processed Interpretation (CPI) for Butmah Formation in the studied wells. In order to check reliability of log data, lithology of the formation was detected using neutron-density cross plot and M-N cross plot. The results of log interpretation display that Butmah Formation is composed of dolomite, limestone with anhydrite and shale in the studied wells. The calculated shale volume shows low clay content. Therefore, Butmah Formation is considered as a clean formation in both wells. Porosity was estimated using porosity logs (sonic, density, and neutron) and had the average total porosity of (7% - 8%) in the two wells. Based on the determined petrophysical properties, particularly porosity and hydrocarbon saturation, it can be concluded that Butmah Formation seems potentially good in reserving oil.

Total organic carbon content deduced from resistivity-porosity logs overlay: a case study of Abu Roash formation, Southwest Qarun field, Gindi Basin, Egypt

ABSTRACT The present work evaluates the hydrocarbon potentiality of Abu Roash Formation in SWQ-25 well, Southwest Qarun (SWQ) Field, Gindi Basin, Egypt. The estimation of total organic carbon content (TOC) has been deduced using the overlay between deep resistivity and porosity logs. The results show that Abu Roash A Member is fair to good source rock (TOC ranges from 0.50 to 1.28 wt. %). While, Abu Roash D Member contains fair to very good TOC values (from 0.77 to 2.92 wt. %). In contrast, Abu Roash B and E members display poor to good source rocks, with TOC varies between 0.36 to 1.74 wt. % and 0.32 to 1.42 wt. %, respectively. However, Abu Roash C Member (with TOC varies between 1.50 and 2.19 wt. %) and Abu Roash F Member (with TOC differs from 1.54 to 3.16 wt. %) indicate a good to very good source rock intervals. The neutron porosity–resistivity overlay matches the density-resistivity overlay in carbonate members (A, B, C, D and F members) but a lack of resemblance in clastic units (E and G members).

Geological and geophysical features of and controls on occurrence and accumulation of gas hydrates in the first offshore gas-hydrate production test region in the Shenhu area, Northern South China Sea

Abstract Integrated three-dimensional seismic, logging, sediment cores, and geochemical testing data were collected from Guangzhou Marine Geological Survey 3 and 4 hydrate drilling expeditions and used in this study for a comprehensive investigation of the geological and geophysical features and accumulation mechanism of hydrates in the first offshore gas-hydrate production test region (GHPTR) in the Shenhu area of the South China Sea. Seismic signatures indicative of disseminated hydrates and free gas include the bottom simulating reflector (BSR), gas chimney, and mud diapir associated with enhanced seismic reflections, acoustic blanking, masking, and chaotic appearance have been observed. The acoustic travel-time responses, density, and compensated neutron three porosity log analysis, high-precision grid tomography inversion analysis, and constrained sparse spike inversion confirm the presence of free gas below the gas-hydrate-bearing zone (GHBZ). Free-gas-bearing zones have significantly different p-wave impedances and low-velocity anomalies than the overlying GHBZ and surrounding strata. These anomalous zones are controlled by the structural attitude of the reservoir strata, which are characterized as inter-bedded stratigraphic units. Variations in the type and geological characteristics of the hydrocarbon migration pathways were observed between sites W18 and W19 on the western ridge and sites W11 and W17 on the eastern ridge in the GMGS study area. The efficiency of gas migration in the western ridge may be higher than that in the eastern ridge, resulting in variations in hydrate gas types, thickness of the GHBZ, and gas migration flux and accumulation. Except for site W11, hydrates were recovered below the structure I inferred BSR at sites W17, W18, and W19. The gas-hydrate stability zone calculations reveal that the structure I hydrate stability zone differs from the BSR depth and is generally shallower than the base of the logging anomaly, indicating the coexistence of structure I and II hydrates. The BSR is not indicative of the BGHSZ; it is rather regarded as a transitional indicator of structure I and II gas hydrates in the GHPTR. The appearance of free gas and hydrates below the structure I inferred BSR indicates that the Shenhu area is characterized by a complex hydrate formation and accumulation system resulting from the supply of biogenic and thermogenic gases. Despite fine-grained host sediments predominating the GHPTR, the coupling of favorable conditions including efficient hydrocarbon generation, sufficient gas supply, multiple pathways for gas migration, and relatively high reservoir porosity have led to the development of highly saturated gas-hydrate accumulations within relatively thick sedimentary sections, which demonstrates a significant resource potential.

Connectionist and mutual information tools to determine water saturation and rank input log variables

Characterization of petroleum reservoirs plays an important role to effectively manage and forecast the recovery performance. A number of subset log variables such as gamma-ray, resistivity, density, neutron, and sonic porosity logs are generally used to characterize/predict the reservoir properties. The data attributes selection and ranking in reservoir characterization are vital to determine the output variables with the best performance and cost-effective manner during exploration and production operations. The objectives of this research work are to estimate the water saturation in the reservoir with an acceptable accuracy and to rank the log variables according to their importance. To achieve the objectives, the mutual information (MI) and artificial neural network (ANN) techniques are implemented with the non-linear predictors using log variables. The feed-forward ANN model is employed and optimized to predict the water saturation, where the Levenberg-Marquardt algorithm is used for the network training. There is a good match between the real data and predictions so that the regression coefficient and the maximum error is 99.98% and 5.55%, respectively. In addition, both ANN and MI approaches lead to the same ranking levels of log variables, implying high accuracy and reliability of the introduced strategies. It is found that the primary (or most important) log variables are the true resistivity and bulk density to obtain the pore fluid saturation. The approach suggested in this study (connectionist and MI strategies) can assist engineers/operators to run a few numbers of logging tools for prediction of water saturation, resulting in saving the exploration costs through an efficient manner. In addition, further understanding is attained to conduct proper data selection for determination of reservoir petrophysical properties.

Integration of sonic and resistivity conventional logs for identification of fracture parameters in the carbonate reservoirs (A case study, Carbonate Asmari Formation, Zagros Basin, SW Iran)

Between all available methods for fractures evaluation, core and image logs are prime methods. However, both of these data are very expensive and practically available in less than 10% of drilled wells. Therefore, a less expensive and quick method would be very helpful for fracture analysis. The main aim of this study is to identify the fracture parameters in the carbonate reservoirs, i.e. fracture density and fracture aperture, using a combination of sonic, resistivity and other conventional logs which commonly available in all wells. For this aim, fractures and their parameters were precisely determined on the image logs in 6 wells from carbonate Asmari Formation as a typical fractured reservoir in the world. Then, the effect of fracture parameters on the sonic and resistivity logs were used to identify fracture parameters in the wells with no core and image logs. Results indicate that the zones with high fracture density and aperture are easily detectable by conventional logs. On the other hand, the behavior of these logs is a function of fracture parameters in the fractured intervals, especially in the zones with high fracture aperture. Separation between shallow (RLA2) and deep (RLA5) resistivity logs along with their cross plot are very reliable indicators for fractured intervals. Sonic transit time rapidly increases where fractures density or aperture increased as well. In this study, a combination of sonic and neutron porosity logs have also been used to determine the effect of fracture aperture, as the main effective factor on the reservoir properties. Furthermore, full waveforms in some studied wells combined with sonic transit time to improve the results of fractures evaluation. Using sonic and resistivity along with neutron porosity and gamma ray logs provides a reliable method for describing the fracture parameters which shows high correlation with image logs results. Due to the high impact of fracture aperture on both reservoir quality and conventional log responses, determination of this parameter is more crucial than other fracture parameters. This method is also applicable for OBM (Oil Base Mud) image tools which conventionally cannot detect fracture aperture.

Fracture-cave carbonate reservoir permeability modelling based on conventional log and well deliverability predication: A case study of the Amu Darya Gas field in Turkmenistan

The carbonate fracture-cave reservoir is developing in carbonate gentle slope reef flat in east Amu Darya Gas field. Due to the serious heterogeneity of fracture-cave reservoirs, it is difficult to evaluate the productivity of different wells and different reservoir sections. Based on conventional log, core, image log and magnetic resonance log. The sensitive parameters contributed to reservoir permeability are studied, and the conventional log, magnetic log and image log of key well are set up to march core permeability and propagate other fracture-cave carbonate wells. Finally based on log porosity and permeability model result, the well deliverability is evaluated and the well deliverability evaluation results are match with the DST results. The Amu Darya Gas field carbonate fracture-cave permeability model and deliverability evaluation flowcharts and methods were build up and can give instructions for other fracture-cave carbonate reservoir.

A Novel Preprocessing Method Based on Variational Mode Decomposition for Reservoir Characterization Using Support Vector Regression

This article proposes a variational mode decomposition (VMD)-based preprocessing scheme to regularize a target porosity signal to improve its prediction from seismic attributes. A workflow consisting of preprocessing, prediction, and postprocessing stages is designed to implement the proposed scheme on a real hydrocarbon field dataset. In the preprocessing stage, the original porosity signal is decomposed into multiple intrinsic mode functions (IMFs). The regularized porosity signal is constructed from the decomposed IMFs by removing some of the higher frequency modes. Three alternate methods—empirical mode decomposition, complementary ensemble empirical mode decomposition, and VMD—are used for decomposition to identify the best regularization technique. In each case, normalized mutual information and entropy are used to regulate the filtering. Then, mapping of the porosity signal and seismic data is carried out by support vector regression (SVR). Four metrics—correlation coefficient, root-mean-square error, absolute error mean, and scatter index—are used to quantify the performance of SVR while using original and regularized porosity signals as targets separately. The tuned SVR parameters are then used to predict synthetic porosity logs over the study area from seismic volumes. Finally, the predicted porosity volume is postprocessed using an improved complex adaptive diffusion filter. The performance of the SVR is also compared with an artificial neural network. This framework can be used to predict different lithological properties from seismic attributes and visualize them over a reservoir area.

Comparative analysis of artificial intelligence techniques for formation pressure prediction while drilling

Formation pressure is a critical formation condition that affects the efficiency and the economy of drilling operations. The knowledge of the formation pressure is significant to control the well. It will assist in avoiding problems associated with drilling operation and decreasing the cost of the drilling operation. It is essential to predict formation pressure accurately prior to the drilling process to prevent circulation loss and kick. Many methods are used to estimate the formation pressure either from log information or drilling parameters. These methods need pressure trends such as abnormal or normal to estimate the formation pressure. Only two papers used artificial intelligence (AI) to estimate the formation pressure by applying only two AI techniques. In this paper, actual field measurements such as log data (formation density (RHOB), porosity (ϕ), and compressional time (Δt)) and drilling parameters (weight on bit (WOB), rotary speed (RPM), penetration rate (ROP), mud density (MW)) were utilized by five techniques of AI to predict the pore pressure. These AI techniques are artificial neural networks (ANN), radial basis function (RBF), fuzzy logic (FL), support vector machine (SVM), and functional networks (FN). In addition, comparative analyses were performed to determine the best combination of AI technique to predict the pore pressure with high accuracy. All artificial intelligence techniques predicted the formation pressure with high performance. In addition, the developed AI methods can be used to estimate the formation pressure without the need for normal pressure trends. The comparative analysis results show that support vector machine has the feature of formation pressure estimation by its short time prediction and high accuracy (a coefficient of determination of 0.995 and an average percentage error of 0.14%). It is recommended to apply SVM to predict the pore pressure in field operations.

Dynamic Rock Type Characterization Using Artificial Neural Networks in Hamra Quartzites Reservoir: A Multidisciplinary Approach

A new multidisciplinary workflow is suggested to re-characterize the Hamra Quartzite (QH) formation using artificial neural networks. This approach involves core description, routine core analysis, special core analysis and raw logs of fourteen wells. An efficient electrofacies clustering neural network technology based on a self-organizing map is performed. The inputs in the model computation are: neutron porosity, gamma ray and bulk density logs. According to the self-organizing map results, the reservoir is composed of five electrofacies (EF1 to EF5): EF1, EF2 and EF3 with good reservoir quality, EF4 with moderate quality, and EF5 with bad quality. Hydraulic flow units are determined from well logs and core data using the flow zone indicator (FZI) approach and the multilayer perception (MLP) method. Obtained results indicate eight optimal hydraulic flow units. Hydraulic flow units for un-cored well are determined using the MLP, the used inputs to train the neural system are: neutron porosity, gamma ray, bulk density and predefined electrofacies. A dynamic rock typing is achieved using the FZI approach and combining special core data analysis to better characterize the hydraulic reservoir behavior. A best-fit relationship between water saturation and J-function is established and a good saturation match is obtained between capillary pressure and interpreted log results.