Environmental corrections to gamma-ray log data: Strategies for geophysical logging with geological and technical drilling

2010 ◽  
Vol 70 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Klaus Lehmann
Keyword(s):  
Gamma Ray ◽  
Log Data ◽  
Geophysical Logging ◽  
Environmental Corrections ◽  
Gamma Ray Log

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.

Towards the multi-scale characterization of braided fluvial geobodies from outcrop, core, ground-penetrating radar and well log data

2018 ◽  
Vol 488 (1) ◽  
pp. 73-95 ◽  
Author(s):  
Luis Miguel Yeste ◽  
Saturnina Henares ◽  
Neil McDougall ◽  
Fernando García-García ◽  
César Viseras
Keyword(s):  
Ground Penetrating Radar ◽  
Gamma Ray ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Random Pattern ◽  
Log Data ◽  
Architectural Elements ◽  
Ground Penetrating ◽  
Gamma Ray Log

AbstractThe integrated application of advanced visualization techniques – validated against outcrop, core and gamma ray log data – was found to be crucial in characterizing the spatial distribution of fluvial facies and their inherent permeability baffles to a centimetre-scale vertical resolution. An outcrop/behind outcrop workflow was used, combining the sedimentological analysis of a perennial deep braided outcrop with ground-penetrating radar profiles, behind outcrop optical and acoustic borehole imaging, and the analyses of dip tadpoles, core and gamma ray logs. Data from both the surface and subsurface allowed the recognition of two main architectural elements – channels and compound bars – and within the latter to distinguish between the bar head and tail and the cross-bar channel. On the basis of a well-constrained sedimentological framework, a detailed characterization of the gamma ray log pattern in the compound bar allowed several differences between the architectural elements to be identified, despite a general cylindrical trend. A high-resolution tadpole analysis showed that a random pattern prevailed in the channel, whereas in the bar head and tail the tadpoles displayed characteristic patterns that allowed differentiation. The ground-penetrating radar profiles aided the 3D reconstruction of each architectural element. Thus the application of this outcrop/behind outcrop workflow provided a solid database for the characterization of reservoir rock properties from outcrop analogues.

THE APPLICATION OF THE SEMIPARAMETRIC GSTAR MODEL IN DETERMINING GAMMA-RAY LOG DATA ON SOIL LAYERS

2021 ◽  
Vol 14 (2) ◽  
pp. 108-117
Author(s):  
Yundari Yundari ◽  
Shantika Martha
Keyword(s):  
Gamma Ray ◽  
Soil Layer ◽  
Small Error ◽  
Observation Data ◽  
Log Data ◽  
Spatial Weights ◽  
Fixed Weight ◽  
Sample Data ◽  
Drill Holes ◽  
Gamma Ray Log

This research examines the semiparametric Generalized Space-Time Autoregressive (GSTAR) spacetime modeling and determines its spatial weight. In general, the spatial weights used are uniform, binary weights, and based on the distance, the result is a fixed weight. The GSTAR model is a stochastic model that takes into account its random variables. Thus, it is necessary to study the random spatial weights. This study introduced a new method to estimate the observed value of the GSTAR model semiparametric with a uniform kernel. The data involved the Gamma Ray (GR) log data on four coal drill holes. The semiparametric GSTAR modeling aimed to predict the amount of log GR in the unobserved soil layer based on the observation data information on the layer above it and its surrounding location. The results revealed that semiparametric GSTAR modeling could predict the presence of coal seams and their thickness of drill holes. The results also highlight the validity test on the out-sample data that the error in each borehole results in a small error. In addition, the error tends to approach the actual observed value at a depth of 1 meter down.

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

scholarly journals Microfaunal Signals and Paleoenvironmental Reconstruction Sediments in Well Z-1, Offshore Dahomey Basin, South-Western Nigeria

2020 ◽  
pp. 50-61
Author(s):  
Anthonia Nwanese Asadu ◽  
Charles Ojonuba Ameh
Keyword(s):  
Gamma Ray ◽  
Accessory Mineral ◽  
Paleoenvironmental Reconstruction ◽  
Planktic Foraminifera ◽  
Rock Samples ◽  
Foraminiferal Species ◽  
Depositional Settings ◽  
Dahomey Basin ◽  
Abundance And Diversity ◽  
Gamma Ray Log

Fifty ditch cutting rock samples from well Z-1, OPL 310 offshore Dahomey basin, south western Nigeria were analyzed for their microfaunal and lithofacies content for the purpose of reconstructing the environment of deposition. Standard techniques of foraminifera slide processing and analysis was followed for the recovery of foraminifera while the gamma ray log complemented the rock samples for the lithofacies analysis. The lithological analysis revealed two lithofacies units in a generally fining upward sequence. The basal sandstone unit is characteristically milky white to brownish, coarse-pebbly grained, sub-angular to round and poorly to well sort with intercalation of shale. This unit is overlain by light to dark grey, moderately hard and non-fissile shale/mudstone sequence with intercalation of sand. Accessory mineral assemblage present in the formations includes mica flakes, glauconite pellets, carbonaceous detritus and ferruginous materials. The basal sandstone unit belong to the Oshosun Formation while the upper shaly unit is typical of Afowo Formation. Microfaunal study showed good recovery of abundant and well diversified planktic and benthic foraminiferal species. Forty-two (42) planktic, sixty-five (65) benthic calcareous and one benthonic arenaceous foraminiferal species were recovered. Micropaleontologically, Paleoenvironmental deductions were based primarily on the assemblage, abundance and diversity of benthic foraminiferal species and presence or absence of planktic foraminifera. Accessory mineral presence also aided the interpretations. Integration of lithological and micropaleontological synthesis enhanced the delineation of two environmental subzones over the analyzed interval, the outer neritic and the upper bathyal depositional settings corresponding to Afowo and Oshosun Formation respectively. A lowstand prograding wedge which is a good exploration target offshore was recognized between intervals 3400 ft to 3500 ft. In conclusion, the rock succession studied, penetrated Afowo and Oshosun Formations, and were deposited in an environment ranging from outer neritic to upper bathyal settings.

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

2020 ◽  
Vol 5 (1) ◽  
pp. 3-14
Author(s):  
Edo Pratama ◽  
Bagus Sapto Mulyatno
Keyword(s):  
Seismic Data ◽  
Gamma Ray ◽  
Regression Method ◽  
Well Test ◽  
Correlation Result ◽  
Neutron Porosity ◽  
Gamma Ray Log ◽  
Porosity Estimation ◽  
Estimation Range ◽  
Test Correlation

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.

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