Discriminating Shale Layers by Pseudo CGR Logs Created Using Artificial Intelligence

2021 ◽  
Author(s):  
Saud K. Aldajani ◽  
Saud F. Alotaibi ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Artificial Intelligence ◽  
Gamma Ray ◽  
Effective Porosity ◽  
Carbonate Reservoir ◽  
Added Value ◽  
Subtractive Clustering ◽  
Neutron Density ◽  
High Porosity ◽  
Inference System ◽  
Porosity Logs

Abstract The discrimination of shale vs. non-shale layers significantly influences the quality of reservoir geological model. In this study, a novel approach was implemented to enhance the model by creating Pseudo Corrected Gamma Ray (CGR) logs using Artificial Intelligence methods to identify the thin shale beds within the reservoir. The lithology of the carbonate reservoir understudy is mostly composed of dolomite and limestone rock with minor amounts of anhydrite and thin shale layers. The identification of shale layers is challenging because of the nature of such reservoirs. The high organic content of the shales and the presence of dolomites, particularly the floatstones and rudstones, can adversely affect the log quality and interpretation and may result in inaccurate log correlations, overestimating/ underestimating Original Oil In Place (OOIP) and reservoir net pays. In such cases, Corrected Gamma Ray (CGR) curves are typically used to identify shale layers. The CGR curve response is due to the combination of thorium and potassium that is associated with the clay content. The difference between the total GR and the CGR is essentially the amount of uranium-associated organic matter. Because of the very limited number of CGR logs in this reservoir, Artificial Intelligence (AI) approach was used to identify shale volume across the entire reservoir. Synthetic CGR curves were generated for the wells lacking CGR logs using AI methods. Resistivity, Density, Neutron and total GR logs were used as inputs while CGR was set as the target. Five wells that have CGR logs were used to train the model. The created pseudo logs were then used to identify shale layers and could also be used to correct effective porosity logs. After statistical analysis of the data, two different Artificial Intelligence Techniques were tested to predict CGR logs; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). A Sugeno-type FIS structure using subtractive clustering demonstrated the best prediction with correlation coefficient of 0.96 and mean absolute percentage error (MAPE) of 20%. The resulting synthetic CGR curves helped identify shale layers that do not extend over the entire reservoir area and ultimately correct the effective porosity logs in the reservoir model. Porosity was primarily obtained from the neutron-density logs which results in very high porosity measurements across the shale layers. This study shows a new workflow to predict shale layers in Carbonate reservoirs. The created pseudo CGR logs would help predict shale and is an added-value data that could be incorporated into the Earth model.

scholarly journals Petrophysical Properties and Digenetic Evolution of Shuaiba Formation in Nasiriyah Oil Field, Southern Iraq

2021 ◽  
pp. 4810-4818
Author(s):  
Marwah H. Khudhair
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Middle Part ◽  
Effective Porosity ◽  
Oil Field ◽  
Neutron Density ◽  
Petrophysical Properties ◽  
Secondary Porosity ◽  
Primary Porosity ◽  
Gamma Ray Log

     Shuaiba Formation is a carbonate succession deposited within Aptian Sequences. This research deals with the petrophysical and reservoir characterizations characteristics of the interval of interest in five wells of the Nasiriyah oil field. The petrophysical properties were determined by using different types of well logs, such as electric logs (LLS, LLD, MFSL), porosity logs (neutron, density, sonic), as well as gamma ray log. The studied sequence was mostly affected by dolomitization, which changed the lithology of the formation to dolostone and enhanced the secondary porosity that replaced the primary porosity. Depending on gamma ray log response and the shale volume, the formation is classified into three zones. These zones are A, B, and C, each can be split into three rock intervals in respect to the bulk porosity measurements. The resulted porosity intervals are: (I) High to medium effective porosity, (II) High to medium inactive porosity, and (III) Low or non-porosity intervals. In relevance to porosity, resistivity, and water saturation points of view, there are two main reservoir horizon intervals within Shuaiba Formation. Both horizons appear in the middle part of the formation, being located within the wells Ns-1, 2, and 3. These intervals are attributed to high to medium effective porosity, low shale content, and high values of the deep resistivity logs. The second horizon appears clearly in Ns-2 well only.

Joint petrophysical interpretation of multidetector nuclear measurements in the presence of invasion, shoulder-bed, and well-deviation effects

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. D13-D30 ◽  
Author(s):  
Edwin Ortega ◽  
Mathilde Luycx ◽  
Carlos Torres-Verdín ◽  
William E. Preeg
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Total Porosity ◽  
Neutron Density ◽  
Layer By Layer ◽  
Fluid Type ◽  
Fluid Properties ◽  
Radial Length ◽  
Neutron Porosity ◽  
Porosity Logs

Recent advances in logging-while-drilling sigma measurements include three-detector thermal-neutron and gamma-ray decay measurements with different radial sensitivities to assess the presence of invasion. We have developed an inversion-based work flow for the joint interpretation of multidetector neutron, density, and sigma logs to reduce invasion, shoulder-bed, and well-deviation effects in the estimation of porosity, water saturation, and hydrocarbon type, whenever the invasion is shallow. The procedure begins with a correction for matrix and fluid effects on neutron and density-porosity logs to estimate porosity. Multidetector time decays are then used to assess the radial length of the invasion and estimate the virgin-zone sigma while simultaneously reducing shoulder-bed and well-deviation effects. Density and neutron porosity logs are corrected for invasion and shoulder-bed effects using two-detector density and neutron measurements with the output from the time-decay (sigma) inversion. The final step invokes a nuclear solver in which corrected sigma, inverse of migration length, and density in the virgin zone are used to estimate water saturation and fluid type. The fluid type is assessed with a flash calculation and Schlumberger’s Nuclear Parameter calculation code to account for the nuclear properties of different types of hydrocarbon and water as a function of pressure, temperature, and salinity. Results indicate that accounting for invasion effects is necessary when using density and neutron logs for petrophysical interpretation beyond the calculation of total porosity. Synthetic and field examples indicate that the mitigation of invasion effects becomes important in the case of salty mud filtrate invading gas-bearing formations. The advantage of the developed inversion-based interpretation method is its ability to estimate layer-by-layer petrophysical, compositional, and fluid properties that honor multiple nuclear measurements, their tool physics, and their associated borehole geometrical and environmental effects.

New Analytical Method To Calculate Matrix- and Fluid-Corrected Total Porosity in Organic Shale

2015 ◽  
Vol 18 (04) ◽  
pp. 609-623
Author(s):  
Edwin Ortega ◽  
Carlos Torres-Verdín
Keyword(s):  
Analytical Method ◽  
Gamma Ray ◽  
Total Porosity ◽  
Matrix Composition ◽  
Neutron Density ◽  
Fluid Density ◽  
Remarkable Feature ◽  
Gamma Ray Spectroscopy ◽  
Porosity Logs ◽  
Organic Shale

Summary Estimation of total porosity from neutron and density porosity logs in organic shale (source rock) is challenging because these logs are substantially affected by fluid and matrix-composition effects. Conventional interpretation of neutron and density porosity logs often includes corrections for shale concentration in which the main objective is to improve the calculation of nonshale porosity in hydrocarbon-bearing zones. These corrections are not desirable in unconventional rock formations because shale pores can be hydrocarbon-saturated. Neutron and density porosity readings across shale zones are sometimes averaged by use of the root-mean-square (RMS) method. We introduce a new and simple analytical expression for total porosity that effectively separates both matrix and fluid effects on neutron and density porosity logs. The expression stems from a new nonlinear mixing law for neutron migration length that is coupled with the linear-density mixing law to calculate total porosity and fluid density. The method is applied in two sequential steps: First, separate corrections for only matrix effects are implemented to enhance the neutron-density crossover for qualitative interpretation of fluid type; second, the coupled equation is used to estimate fluid density and actual porosity devoid of matrix and fluid effects. Calculated porosity and fluid density can be used further to calculate water saturation from density logs. One remarkable feature of this method is the ease with which it can be applied to obtain accurate and reliable results. Application of the method only requires knowledge of single-component nuclear properties and mineral volumetric concentrations. One can obtain nuclear properties from a set of charts for multiple fluid types and minerals provided in this paper, whereas one can calculate mineral concentrations on the basis of available triple combo logs or gamma ray spectroscopy logs. Two synthetic and four field examples (two conventional and two shale-gas reservoirs) are used to test the method. First, we describe an application in a conventional siliciclastic sedimentary sequence in which only shale concentration calculated from gamma ray logs is required to improve the estimation of porosity in shaly sections. Second, we document several applications in which gamma ray spectroscopy logs are used together with a reliable hypothesis for clay type to define mineral properties. Results compare well with nuclear-magnetic-resonance (NMR) and core measurements, whereas the new method outperforms the conventional RMS procedure, especially in the cases of gas-bearing, low-porosity organic shale. The new analytical method can be readily implemented on an Excel spreadsheet and requires minimal adjustments for its operation.

scholarly journals Characterization of Mishrif Formation Reservoir in Amara Oil Field, Southeast Iraq, Using Geophysical Well-logging

2021 ◽  
pp. 4702-4711
Author(s):  
Asmaa Talal Fadel ◽  
Madhat E. Nasser
Keyword(s):  
Reservoir Characterization ◽  
Gamma Ray ◽  
Water Saturation ◽  
Total Porosity ◽  
Effective Porosity ◽  
Oil Field ◽  
Neutron Density ◽  
Reservoir Water ◽  
Resistivity Logs ◽  
Reliable Knowledge

     Reservoir characterization requires reliable knowledge of certain fundamental properties of the reservoir. These properties can be defined or at least inferred by log measurements, including porosity, resistivity, volume of shale, lithology, water saturation, and permeability of oil or gas. The current research is an estimate of the reservoir characteristics of Mishrif Formation in Amara Oil Field, particularly well AM-1, in south eastern Iraq. Mishrif Formation (Cenomanin-Early Touronin) is considered as the prime reservoir in Amara Oil Field. The Formation is divided into three reservoir units (MA, MB, MC). The unit MB is divided into two secondary units (MB1, MB2) while the unit MC is also divided into two secondary units (MC1, MC2). Using Geoframe software, the available well log images (sonic, density, neutron, gamma ray, spontaneous potential, and resistivity logs) were digitized and updated. Petrophysical properties, such as porosity, saturation of water, saturation of hydrocarbon, etc. were calculated and explained. The total porosity was measured using the density and neutron log, and then corrected to measure the effective porosity by the volume content of clay. Neutron -density cross-plot showed that Mishrif Formation lithology consists predominantly of limestone. The reservoir water resistivity (Rw) values of the Formation were calculated using Pickett-Plot method.   

scholarly journals Evaluation of Mishrif Reservoir in Abu Amood Oil Field, Southern Iraq

2021 ◽  
pp. 4758-4768
Author(s):  
Ahmed Hussain ◽  
Medhat E. Nasser ◽  
Ghazi Hassan
Keyword(s):  
Total Porosity ◽  
Effective Porosity ◽  
Oil Field ◽  
Well Logs ◽  
Neutron Density ◽  
High Porosity ◽  
Petrophysical Properties ◽  
Density Method ◽  
Southern Iraq

     The main goal of this study is to evaluate Mishrif Reservoir in Abu Amood oil field, southern Iraq, using the available well logs. The sets of logs were acquired for wells AAm-1, AAm-2, AAm-3, AAm-4, and AAm-5. The evaluation included the identification of the reservoir units and the calculation of their petrophysical properties using the Techlog software. Total porosity was calculated using the neutron-density method and the values were corrected from the volume of shale in order to calculate the effective porosity. Computer processed interpretation (CPI) was accomplished for the five wells. The results show that Mishrif Formation in Abu Amood field consists of three reservoir units with various percentages of hydrocarbons that were concentrated in all of the three units, but in different wells. All of the units have high porosity, especially unit two, although it is saturated with water.

scholarly journals Estimation of Oil Recovery Factor for Water Drive Sandy Reservoirs through Applications of Artificial Intelligence

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3671 ◽  
Author(s):  
Ahmed Mahmoud ◽  
Salaheldin Elkatatny ◽  
Weiqing Chen ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Artificial Intelligence ◽  
Oil Recovery ◽  
Water Saturation ◽  
Data Availability ◽  
Recovery Factor ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Subtractive Clustering ◽  
Oil Viscosity ◽  
Inference System

Hydrocarbon reserve evaluation is the major concern for all oil and gas operating companies. Nowadays, the estimation of oil recovery factor (RF) could be achieved through several techniques. The accuracy of these techniques depends on data availability, which is strongly dependent on the reservoir age. In this study, 10 parameters accessible in the early reservoir life are considered for RF estimation using four artificial intelligence (AI) techniques. These parameters are the net pay (effective reservoir thickness), stock-tank oil initially in place, original reservoir pressure, asset area (reservoir area), porosity, Lorenz coefficient, effective permeability, API gravity, oil viscosity, and initial water saturation. The AI techniques used are the artificial neural networks (ANNs), radial basis neuron networks, adaptive neuro-fuzzy inference system with subtractive clustering, and support vector machines. AI models were trained using data collected from 130 water drive sandstone reservoirs; then, an empirical correlation for RF estimation was developed based on the trained ANN model’s weights and biases. Data collected from another 38 reservoirs were used to test the predictability of the suggested AI models and the ANNs-based correlation; then, performance of the ANNs-based correlation was compared with three of the currently available empirical equations for RF estimation. The developed ANNs-based equation outperformed the available equations in terms of all the measures of error evaluation considered in this study, and also has the highest coefficient of determination of 0.94 compared to only 0.55 obtained from Gulstad correlation, which is one of the most accurate correlations currently available.

scholarly journals Evaluation of Petrophysical Properties Using Well Logs of Yamama Formation in Abu Amood Oil Field, Southern Iraq

2021 ◽  
Vol 54 (1E) ◽  
pp. 67-77
Author(s):  
Buraq Adnan Al-Baldawi
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Bulk Water ◽  
Carbonate Reservoir ◽  
Oil Field ◽  
Well Logs ◽  
Water Volume ◽  
High Porosity ◽  
Petrophysical Properties ◽  
Reservoir Properties

The petrophysical analysis is very important to understand the factors controlling the reservoir quality and production wells. In the current study, the petrophysical evaluation was accomplished to hydrocarbon assessment based on well log data of four wells of Early Cretaceous carbonate reservoir Yamama Formation in Abu-Amood oil field in the southern part of Iraq. The available well logs such as sonic, density, neutron, gamma ray, SP, and resistivity logs for wells AAm-1, AAm-2, AAm-3, and AAm-5 were used to delineate the reservoir characteristics of the Yamama Formation. Lithologic and mineralogic studies were performed using porosity logs combination cross plots such as density vs. neutron cross plot and M-N mineralogy plot. These cross plots show that the Yamama Formation consists mainly of limestone and the essential mineral components are dominantly calcite with small amounts of dolomite. The petrophysical characteristics such as porosity, water and hydrocarbon saturation and bulk water volume were determined and interpreted using Techlog software to carried out and building the full computer processed interpretation for reservoir properties. Based on the petrophysical properties of studied wells, the Yamama Formation is divided into six units; (YB-1, YB-2, YB-3, YC-1, YC-2 and YC-3) separated by dense non porous units (Barrier beds). The units (YB-1, YB-2, YC-2 and YC-3) represent the most important reservoir units and oil-bearing zones because these reservoir units are characterized by good petrophysical properties due to high porosity and low to moderate water saturation. The other units are not reservoirs and not oil-bearing units due to low porosity and high-water saturation.

scholarly journals Shale volume and porosity delineation of coast swamp depobelt in Niger delta region, Nigeria, using well log

2019 ◽  
Vol 7 (2) ◽  
pp. 142
Author(s):  
Ubong Essien
Keyword(s):  
Niger Delta ◽  
Gamma Ray ◽  
Water Saturation ◽  
Effective Porosity ◽  
Neutron Density ◽  
Well Log ◽  
Shale Formations ◽  
Log Data ◽  
Shale Volume ◽  
Niger Delta Region

Well log data from two wells were evaluated for shale volume, total and effective porosity. Well log data were obtained from gamma ray, neutron-density log, resistivity, sonic and caliper log respectively. This study aimed at evaluating the effect of shale volume, total and effective porosity form two well log data. The results of the analysis depict the presence of sand, sand-shale and shale formations. Hydrocarbon accumulation were found to be high in sand, fair in sand-shale and low in shale, since existence of shale reduces total and effective porosity and water saturation of the reservoir. The thickness of the reservoir ranged from 66 – 248.5ft. The average values of volume of shale, total and effective porosity values ranged from 0.004 – 0.299dec, 0.178 – 0.207dec and 0.154 – 0.194dec. Similarly, the water saturation and permeability ranged from 0.277 – 0.447dec and 36.637 - 7808.519md respectively. These values of total and effective porosity are high in sand, fair in sand-shale and low in shale formations. The results for this study demonstrate: accuracy, applicability of these approaches and enhance the proper evaluation of petrophysical parameters from well log data.    

scholarly journals Sandstone reservoir characteristics of Rio Del Rey basin, Cameroon, using well-logging analysis

2021 ◽  
Author(s):  
Janvier Domra Kana ◽  
Ahmad Diab Ahmad ◽  
Daniel Hervé Gouet ◽  
Xavier Djimhoudouel ◽  
Serge Parfait Koah Na Lebogo
Keyword(s):  
Oil And Gas ◽  
Gamma Ray ◽  
Water Saturation ◽  
Effective Porosity ◽  
High Resistivity ◽  
Neutron Density ◽  
Oil And Gas Exploration ◽  
Hydrocarbon Reservoir ◽  
Offshore Oil And Gas ◽  
Cloud Of Points

AbstractThe present work deals with an interpretation of well log data (gamma ray (GR), resistivity, density, and neutron) from four wells, namely P-1, P-2, P-3 and P-4 in the study area of the Rio Del Rey basin. The well logs analysis indicates five potential sandstone reservoirs at the P-1, two at the P-2, four at the P-3 and six at the P-4. The neutron–density-GR logs highlight the sandstone gas reservoir characterized by high resistivity and crossover between neutron density. The neutron–density-GR cross-plot confirms the presence of sandstone containing hydrocarbons by a displacement of the cloud of points, from low to medium GR values, from the sandstone line to the left. Petrophysical parameters exhibit the value 12–41% for a volume of shale, 15–34% for effective porosity, 29–278 mD for permeability and 3–63% for water saturation. The three potential hydrocarbon reservoir saturation ranges from 22 to 45%. The study will contribute to future offshore oil and gas exploration and development in the Rio Del Rey basin, based on the geological and geophysical characteristics of the reservoirs delineated.

scholarly journals Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative study

2019 ◽  
Vol 9 (7) ◽  
Author(s):  
Salim Heddam ◽  
Hadi Sanikhani ◽  
Ozgur Kisi
Keyword(s):  
Artificial Intelligence ◽  
Water Quality ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Test Phase ◽  
Pigment Concentration ◽  
Quality Data ◽  
Subtractive Clustering ◽  
Inference System ◽  
Neuro Fuzzy

Abstract In the present investigation, the usefulness and capabilities of four artificial intelligence (AI) models, namely feedforward neural networks (FFNNs), gene expression programming (GEP), adaptive neuro-fuzzy inference system with grid partition (ANFIS-GP) and adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC), were investigated in an attempt to evaluate their predictive ability of the phycocyanin pigment concentration (PC) using data from two stations operated by the United States Geological Survey (USGS). Four water quality parameters, namely temperature, pH, specific conductance and dissolved oxygen, were utilized for PC concentration estimation. The four models were evaluated using root mean square errors (RMSEs), mean absolute errors (MAEs) and correlation coefficient (R). The results showed that the ANFIS-SC provided more accurate predictions in comparison with ANFIS-GP, GEP and FFNN for both stations. For USGS 06892350 station, the R, RMSE and MAE values in the test phase for ANFIS-SC were 0.955, 0.205 μg/L and 0.148 μg/L, respectively. Similarly, for USGS 14211720 station, the R, RMSE and MAE values in the test phase for ANFIS-SC, respectively, were 0.950, 0.050 μg/L and 0.031 μg/L. Also, using several combinations of the input variables, the results showed that the ANFIS-SC having only temperature and pH as inputs provided good accuracy, with R, RMSE and MAE values in the test phase, respectively, equal to 0.917, 0.275 μg/L and 0.200 μg/L for USGS 06892350 station. This study proved that artificial intelligence models are good and powerful tools for predicting PC concentration using only water quality variables as predictors.

Sign in / Sign up

Export Citation Format

Share Document