Autocorrelation Analysis of Gamma Ray Log of Ras Fanar Oil Field in Egypt

2019 ◽  
Vol 13 (3) ◽  
pp. 481-487
Author(s):  
A., Atef, K. S. Sayed, El-Taher, Al-Mugren
Keyword(s):  
Gamma Ray ◽  
Oil Field ◽  
Autocorrelation Analysis ◽  
Gamma Ray Log

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.

scholarly journals Depositional Environment and facies analysis of Useni- 1, Niger Delta Basin, using well logs, core data

2019 ◽  
Vol 7 (1) ◽  
pp. 58
Author(s):  
G. O. Aigbadon ◽  
E. O. Akpunonu ◽  
S. O. Agunloye ◽  
A. Ocheli ◽  
O. O .Akakaru
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Oil Field ◽  
Well Logs ◽  
Depth Range ◽  
Depositional Facies ◽  
Core Data ◽  
Delta Plain ◽  
Niger Delta Basin ◽  
Gamma Ray Log

This study was carried out integrating well logs and core to build reservoir model for the Useni-1 oil field. Core data and well logs were used to evaluate the petrophysical characteristics of the reservoirs. The paleodepositional environment was deduce from the wells and cores data. The depositional facies model showed highly permeable channels where the wells where positioned. The environments identified that the fluvial channel facies with highly permeable zones constituted the reservoirs. Four reservoirs were mapped at depth range of 8000ft to 8400ft with thicknesses varying from 20ft to 400ft. Petrophysical results showed that porosity of the reservoirs varied from 12% to 28 %; permeability from 145.70 md to 454.70md; water saturation from 21.65% to 54.50% and hydrocarbon saturation from 45.50% to 78.50 %. Core data and the gamma ray log trends with right boxcar trend indicate fluvial point bar and tidal channel fills in the lower delta plain setting. By-passed hydrocarbons were identified in low resistivity pay sands D1, D2 at depth of 7800 – 78100ft in the field.  

Pressure Prediction Technology of the Deep Strata Based on BP Neural Network

2010 ◽  
Vol 143-144 ◽  
pp. 28-31 ◽  
Author(s):  
Wei Li ◽  
Tie Yan ◽  
Ying Jie Liang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Gamma Ray ◽  
Forecast Model ◽  
Oil Field ◽  
The Neural Network ◽  
Natural Gamma ◽  
Gamma Ray Log ◽  
Deep Strata ◽  
Very High

. The accurate prediction of strata pressure is the base for safely, quality and efficiently drilling, decreasing hole problems and reasonable development of the reservoir. Because of the high cost, long cycle of the formation pressure measured method, which may influence the safety of drilling operation, thus a new method for predicting strata pressure, based on the BP neural network, is presented in this paper, and establishing process of the neural network forecast model are discussed in detail. This method takes the acoustic time, natural potential, natural gamma ray log data and pipe pressure test data as study sample, which has a very high accuracy. The paper predicts strata pressure of the Saertu oil field and Xingshugang oil field in Daqing, and the results show that relative error between the predicted data and experimental data is less than ±8.9%.

scholarly journals The Use of Gamma Ray Log Data to Compute and Study the Deposition of Shale Volume of Mishrif Formation in Middle and Southern Parts of Iraq

2021 ◽  
pp. 3932-3941
Author(s):  
Hiba Tarq Jaleel ◽  
Ahmed S. Al-Banna ◽  
Ghazi H. Al-Sharaa
Keyword(s):  
Gamma Ray ◽  
Oil Field ◽  
Log Data ◽  
Shale Volume ◽  
Carbonate Formation ◽  
Regional Area ◽  
Isopach Map ◽  
Gamma Ray Log ◽  
Surfer Software

The shale volume is one of the most important properties that can be computed depending on gamma ray log. The shale volume of Mishrif Formation (carbonate formation from middle Cenomanian- early Turonian) was studied for the regional area of the middle and southern parts of Iraq. The gamma ray log data from seventeen  wells ( Kf-3,Kf-4, Ad-1,Ad -2,Dh-1, Bu-47, Ns-2, Ns-4, Am-1,Am-2,Hf-2,Hf-115,Mj-3,Mj-15, Su-7,Wq-15 and  Lu-7) distributed in the study area were used to compute the shale volume of Mishrif Formation. From the available data of the considered wells, a regional isopach map of Mishrif Formation was obtained. The isopach map indicates that the maximum thickness of Mishrif Formation is located at the eastern part of the study area. The results of the CPI and the shale volume map, which were computed using the Techlog and surfer software,  show that the maximum value of shale volume is located at the southern part of the study area (Su-7  well), while the minimum value is at the eastern  part (Hf-2well). According to the classification of Kamel and Mabrouk (2003), Mishrif Formation seems to be a Shaly Formation in the study area, except Halfaya oil field at the eastern part of the study area, which seems as a Clear Formation. The top map of the shale marker bed, which appears in most studied wells, shows a regional trend of the formation toward the northeast. According to the variation of the thickness of the shale marker bed, the study area is divided into four zones.

scholarly journals Lithological Study of Mishrif, Rumaila, Ahmadi and Mauddud Formations Using Vp/ Vs Ratio, Acoustic Impedance and Well Logging at Amara Oil Field, Southern Iraq

2021 ◽  
Vol 54 (2D) ◽  
pp. 39-58
Author(s):  
Hiba Tareq
Keyword(s):  
Acoustic Impedance ◽  
Gamma Ray ◽  
Water Saturation ◽  
Rock Physics ◽  
Shear Velocity ◽  
Oil Field ◽  
High Porosity ◽  
Shale Volume ◽  
Sonic Logs ◽  
Gamma Ray Log

The lithology of four formations from the Cretaceous period (Mishrif, Rumaila, Ahmadi, and Mauddud) was evaluated using the Acoustic Impedance and Vp/Vs ratio cross plot from Rock Physics Templates. Dipole sonic logs in Am-6-Am-10 well log were used to calculate compression velocity then the estimated shear velocity using Greenberg Castagna equations. RHOB and VP logs were used to calculate Acoustic Impedance. The ratio of Vp/Vs was measured then used with Acoustic Impedance colored by shale volume which is measured from gamma ray log, porosity and water saturation to estimate lithology type of the considered formations using cross plots and rock physics chart in the Techlog software. The lithology of the formations found to be of high porosity limestone alternating with hard limestone layers and the shale volume increases in the Ahmadi formation. The water bearing zone was found in all Formations, this zone is indicted by high Vp/ Vs ratio and low AI. The hydrocarbon bearing zones were indicated by low amount of both Acoustic Impedance and Vp/Vs ratio and this observation was shown in Mishrif and Mauddud formations.

scholarly journals Facies Architecture Analysis for Paleo-environment Evaluation in “Tom” Oil Field, Eastern Niger Delta, Nigeria

2020 ◽  
Vol 24 (2) ◽  
pp. 213-221
Author(s):  
T.M. Asubiojo
Keyword(s):  
Niger Delta ◽  
Depositional Environment ◽  
Gamma Ray ◽  
Sedimentary Facies ◽  
Oil Field ◽  
Sedimentary Structures ◽  
Fossil Records ◽  
Depositional Setting ◽  
Gamma Ray Log ◽  
Architecture Analysis

The cored section of reservoir C, well 4 of the drilled five wells that penetrated three reservoirs A, B and C in “TOM” oil field, Eastern Niger Delta was analysed and described on the basis of lithofacies, sedimentary structures and trace fossil records by using core data and wireline log motifs, with  the aim of carrying out thorough geological core analysis to interpret the depositional environment of the oil field. The lithofacies are sandstones  with interbedded mudstones and siltstones, the dominant sedimentary structures are parallel to ripple cross laminations, hummocky and swaley cross stratifications, sandy hetherolitics, planar to low angle cross bedding with traces of Teichichnus and Ophiomorpha burrows. The gamma-ray log motifs were noted and used to further constrain the character of the sedimentary facies and depositional environment of the field. A tidal incised – fluvial dominated shallow marine (lower, middle, upper shoreface) comprises of tidal channel sands and tidal flat of the coastal shelf depositional setting in the marginal marine mega depositional environment had been inferred for the “TOM” field. Keywords: Shoreface, Reservoir, Lithofacies, Structures

Bayesian deconvolution of gamma‐ray logs

Geophysics ◽  
1987 ◽  
Vol 52 (11) ◽  
pp. 1535-1546 ◽  
Author(s):  
Ping Sheng ◽  
Benjamin White ◽  
Balan Nair ◽  
Sandra Kerford
Keyword(s):  
High Frequency ◽  
Gamma Ray ◽  
Simulated Data ◽  
Field Measurements ◽  
Bayesian Optimization ◽  
Frequency Noise ◽  
High Frequency Noise ◽  
Data Application ◽  
Gamma Ray Detector ◽  
Gamma Ray Log

The spatial resolution of gamma‐ray logs is defined by the length 𝓁 of the gamma‐ray detector. To resolve thin beds whose thickness is less than 𝓁, it is generally desirable to deconvolve the data to reduce the averaging effect of the detector. However, inherent in the deconvolution operation is an amplification of high‐frequency noise, which can be a detriment to the intended goal of increased resolution. We propose a Bayesian statistical approach to gamma‐ray log deconvolution which is based on optimization of a probability function which takes into account the statistics of gamma‐ray log measurements as well as the empirical information derived from the data. Application of this method to simulated data and to field measurements shows that it is effective in suppressing high‐frequency noise encountered in the deconvolution of gamma‐ray logs. In particular, a comparison with the least‐squares deconvolution approach indicates that the incorporation of physical and statistical information in the Bayesian optimization process results in optimal filtering of the deconvolved results.

scholarly journals Sequence Stratigraphy of Well DX, Gongola Sub-Basin, Upper Benue Trough, Nigeria

2019 ◽  
Vol 23 (10) ◽  
pp. 1855-1860
Author(s):  
F.O. Amiewalan ◽  
E.O. Bamigboye
Keyword(s):  
Gamma Ray ◽  
Third Order ◽  
Depositional Sequence ◽  
Benue Trough ◽  
Sequence Stratigraphic ◽  
Surface System ◽  
Sequence Boundaries ◽  
System Tracts ◽  
Maximum Flooding Surface ◽  
Gamma Ray Log

: Biostratigraphic study of Well DX has yielded Cretaceous miospores and dinoflagellates cysts which heightened the recognition of sequence boundaries (SB), Maximum Flooding Surfaces (MFS) and associated Systems Tracts. Five maximum flooding surfaces between 95.6 Ma and 89.0 Ma, four sequence boundaries between 96.4 Ma and 93.0 Ma and threedepositional sequences were identified with varying average thicknesses of sediments interpreted from the gamma ray log and biostratigraphic data. The threedepositional sequences interpreted are -depositional sequence I (96.4 Ma - 95.4 Ma) (8240 ft. - 8120 ft.), depositional sequence II (95.4 Ma - 94.0 Ma) (8120 ft. - 7850 ft.) and depositionalsequence III (94.0 Ma - 93.0 Ma) (7850 ft. - 7550 ft.). All the depositional sequences fall within the third order cycle. The age of the well was attempted based on the presence of some selected marker fossils - Ephedripites spp., Classopollis spp., Spiniferites spp., Cyclonephelium distinctum, Cyclonephelium vannophorum, Subtilisphaera spp., Eucomiidites spp., Triorites africaensis, Odontochitina costata and Droseridites senonicus recovered from the studied intervals and was dated Albian - Santonian. The Sequence stratigraphic interpretations are useful in further deepening the knowledge of thesubsurface geology of the studiedwell in Gongola Sub Basin, Upper Benue Trough of Nigeria.Keywords: Sequence Boundary, Maximum Flooding Surface, System tracts, Depositional sequence

scholarly journals A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs

2021 ◽  
Vol 2 (2) ◽  
pp. 82-99
Author(s):  
Mohsen Talebkeikhah ◽  
Zahra Sadeghtabaghi ◽  
Mehdi Shabani
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Gamma Ray ◽  
Accurate Determination ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Learning Approaches ◽  
Statistical Parameters ◽  
Southwest Of Iran ◽  
Gamma Ray Log

Permeability is a vital parameter in reservoir engineering that affects production directly. Since this parameter's significance is obvious, finding a way for accurate determination of permeability is essential as well. In this paper, the permeability of two notable carbonate reservoirs (Ilam and Sarvak) in the southwest of Iran was predicted by several different methods, and the level of accuracy in all models was compared. For this purpose, Multi-Layer Perceptron Neural Network (MLP), Radial Basis Function Neural Network (RBF), Support Vector Regression (SVR), decision tree (DT), and random forest (RF) methods were chosen. The full set of real well-logging data was investigated by random forest, and five of them were selected as the potent variables. Depth, Computed gamma-ray log (CGR), Spectral gamma-ray log (SGR), Neutron porosity log (NPHI), and density log (RHOB) were considered efficacious variables and used as input data, while permeability was considered output. It should be noted that permeability values are derived from core analysis. Statistical parameters like the coefficient of determination ( ), root mean square error (RMSE) and standard deviation (SD) were determined for the train, test, and total sets. Based on statistical and graphical results, the SVM and DT models perform more accurately than others. RMSE, SD and R2values of SVM and DT models are 0.38, 1.63, 0.97 and 0.44, 2.89, and 0.96 respectively. The results of the best-proposed models of this paper were then compared with the outcome of the empirical equation for permeability prediction. The comparison indicates that artificial intelligence methods perform more accurately than traditional methods for permeability estimation, such as proposed equations. Doi: 10.28991/HEF-2021-02-02-01 Full Text: PDF

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