LOG EVALUATION OF NONMETALLIC MINERAL DEPOSITS

Geophysics ◽  
1970 ◽  
Vol 35 (1) ◽  
pp. 124-142 ◽  
Author(s):  
M. P. Tixier ◽  
R. P. Alger
Keyword(s):  
Gamma Ray ◽  
Mineral Deposits ◽  
Good Resolution ◽  
Coal Beds ◽  
Resistivity Logs ◽  
Bulk Volume ◽  
Neutron Porosity ◽  
Commercially Important ◽  
Sulfur Deposits ◽  
Sonic Logs

Well logs can be used to locate and evaluate deposits of various commercially important minerals. It is only necessary that the mineral of interest represent a significant fraction of the formation bulk volume, and that it exhibit characterizing properties measurable by logs. Because modern logging methods measure electrical, density, acoustic, radioactivity, and certain nuclear characteristics of formations, they may be used to identify many minerals. For evaluation of sulfur deposits, either density or sonic logs provide good resolution when compared with porosity computed from neutron or resistivity logs. Trona beds are identified by a sonic reading of approximately 65 μsec/ft, neutron porosity index of about 40 percent, low natural radioactivity, and pronounced hole enlargement. Gamma‐ray logs provide important information in the location, identification, and evaluation of potash mineral deposits. Neutron, sonic, and density logs, in various combinations, augment the gamma‐ray data in such studies. Coal beds are characterized by high resistivities, and by high apparent porosities on sonic, neutron, and density logs. Density logs are particularly suited for evaluation of yield from oil shales. In all such explorations for nonmetallic mineral deposits, well‐logging methods provide a fast, detailed, and economical reconnaissance of the entire length of drilled hole. Results compare well with core assays.

Evaluation of Reservoir’s Petrophysical Parameters, Niger Delta, Nigeria

2017 ◽  
Vol 5 (1) ◽  
pp. 19
Author(s):  
Ubong Essien ◽  
Akaninyene Akankpo ◽  
Okechukwu Agbasi
Keyword(s):  
Niger Delta ◽  
Gamma Ray ◽  
Water Saturation ◽  
Petrophysical Parameters ◽  
Niger Delta Region ◽  
Average Porosity ◽  
Irreducible Water Saturation ◽  
Open Hole ◽  
Bulk Volume ◽  
Niger Delta Basin

Petrophysical analysis was performed in two wells in the Niger Delta Region, Nigeria. This study is aimed at making available petrophysical data, basically water saturation calculation using cementation values of 2.0 for the reservoir formations of two wells in the Niger delta basin. A suite of geophysical open hole logs namely Gamma ray; Resistivity, Sonic, Caliper and Density were used to determine petrophysical parameters. The parameters determined are; volume of shale, porosity, water saturation, irreducible water saturation and bulk volume of water. The thickness of the reservoir varies between 127ft and 1620ft. Average porosity values vary between 0.061 and 0.600; generally decreasing with depth. The mean average computed values for the Petrophysical parameters for the reservoirs are: Bulk Volume of Water, 0.070 to 0.175; Apparent Water Resistivity, 0.239 to 7.969; Water Saturation, 0.229 to 0.749; Irreducible Water Saturation, 0.229 to 0.882 and Volume of Shale, 0.045 to 0.355. The findings will also enhance the proper characterization of the reservoir sands.

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.

scholarly journals Petrophysical Properties of Nahr Umar Formation in Nasiriya Oil Field

2020 ◽  
Vol 21 (3) ◽  
pp. 9-18
Author(s):  
Ahmed Abdulwahhab Suhail ◽  
Mohammed H. Hafiz ◽  
Fadhil S. Kadhim
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Oil Field ◽  
Well Logs ◽  
High Porosity ◽  
Reservoir Properties ◽  
Petrophysical Parameters ◽  
Resistivity Logs ◽  
Lithology Prediction ◽  
Porosity And Permeability

   Petrophysical characterization is the most important stage in reservoir management. The main purpose of this study is to evaluate reservoir properties and lithological identification of Nahr Umar Formation in Nasiriya oil field. The available well logs are (sonic, density, neutron, gamma-ray, SP, and resistivity logs). The petrophysical parameters such as the volume of clay, porosity, permeability, water saturation, were computed and interpreted using IP4.4 software. The lithology prediction of Nahr Umar formation was carried out by sonic -density cross plot technique. Nahr Umar Formation was divided into five units based on well logs interpretation and petrophysical Analysis: Nu-1 to Nu-5. The formation lithology is mainly composed of sandstone interlaminated with shale according to the interpretation of density, sonic, and gamma-ray logs. Interpretation of formation lithology and petrophysical parameters shows that Nu-1 is characterized by low shale content with high porosity and low water saturation whereas Nu-2 and Nu-4 consist mainly of high laminated shale with low porosity and permeability. Nu-3 is high porosity and water saturation and Nu-5 consists mainly of limestone layer that represents the water zone.

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.

Interpretation of porosity and fluid constituents from well logs using an interactive neutron-density matrix scale

2013 ◽  
Vol 1 (2) ◽  
pp. T143-T155 ◽  
Author(s):  
Olabode Ijasan ◽  
Carlos Torres-Verdín ◽  
William E. Preeg
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Matrix Effects ◽  
Neutron Density ◽  
Reservoir Capacity ◽  
Constant Matrix ◽  
Light Oil ◽  
Saturated Formations ◽  
Neutron Porosity ◽  
Interactive Variable

Neutron and density logs are important borehole measurements for estimating reservoir capacity and inferring saturating fluids. The neutron log, measuring the hydrogen index, is commonly expressed in apparent water-filled porosity units assuming a constant matrix lithology whereby it is not always representative of actual pore fluid. By contrast, a lithology-independent porosity calculation from nuclear magnetic resonance (NMR) and/or core measurements provides reliable evaluations of reservoir capacity. In practice, not all wells include core or NMR measurements. We discovered an interpretation workflow wherein formation porosity and hydrocarbon constituents can be estimated from density and neutron logs using an interactive, variable matrix scale specifically suited for the precalculated matrix density. First, we estimated matrix components from combinations of nuclear logs (photoelectric factor, spontaneous gamma ray, neutron, and density) using Schlumberger’s nuclear parameter calculator (SNUPAR) as a matrix compositional solver while assuming freshwater-filled formations. The combined effects of grain density, volumetric concentration of shale, matrix hydrogen, and neutron lithology units define an interactive matrix scale for correction of neutron porosity. Under updated matrix conditions, the resulting neutron-density crossover can only be attributed to pore volume and saturating fluid effects. Second, porosity, connate-water saturation, and hydrocarbon density are calculated from the discrepancy between corrected neutron and density logs using SNUPAR and Archie’s water saturation equation, thereby eliminating the assumption of freshwater saturation. With matrix effects eliminated from the neutron-density overlay, gas- or light-oil-saturated formations exhibiting the characteristic gas neutron-density crossover become representative of saturating hydrocarbons. This behavior gives a clear qualitative distinction between hydrocarbon-saturated and nonviable depth zones.

scholarly journals GEOPHYSICAL AND WELL CORELLATION ANALYSIS OF OGO FIELD: A CASE STUDY IN NIGER DELTA BASIN OF NIGERIA

2017 ◽  
Vol 36 (3) ◽  
pp. 729-733
Author(s):  
MO Ehigiator ◽  
NC Chigbata
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Average Porosity ◽  
Resistivity Logs ◽  
Hydrocarbon Formation ◽  
Formation Resistivity ◽  
Well Correlation ◽  
Niger Delta Basin ◽  
4D Seismic ◽  
Bottom To Top

A suite of geophysical wire line logs were run in hole. The wells data were acquired from bottom to top and not top to bottom. Basically, we have the qualitative and the quantitative evaluation techniques.Qualitative means is usually used for identification of the type of lithology and also for the component of the formation. Quantitative is used to estimate numerically, the value of what is in the formation. The logs used for evaluation were: Spontaneous potential logs and the Gamma ray logs. These were used to determine the lithology of the formation. Resistivity logs were run in hole to also determine the water saturation in the formation. The Formation Density and the compensated Neutron logs were run in hole to differentiate the gaseous zone from the oil zone in the Hydrocarbon Formation Ogo1, Ogo2 and Ogo3 from well correlation depicts that the subsurface stratigraphy is that of sand – shale intercalations.  Two prominent hydrocarbon bearing reservoirs (R1and R2), at Depth 1563m and 1642mm respectively were identified. The reservoirs were found to have average porosity of 0.22, water saturation 0.43 and Hydrocarbon saturation of 0.57. The reservoirs have permeability of 1376m, volume of oil in place for reservoir 1 and 2 is 39900m3  and 9647 m3   respectively. More. Well correlations are recommended for proper drilling and well completions. 4D seismic acquisitions should be encouraged for proper view of the formation. http://dx.doi.org/10.4314/njt.v36i3.10

scholarly journals 3-D Seismic Structural Interpretation of High Field Offshore Western Niger Delta

2021 ◽  
Vol 25 (8) ◽  
pp. 1361-1369
Author(s):  
S.S. Adebayo ◽  
E.O. Agbalagba ◽  
A.I. Korode ◽  
T.S. Fagbemigun ◽  
O.E. Oyanameh ◽  
...  
Keyword(s):  
Niger Delta ◽  
High Field ◽  
Gamma Ray ◽  
Tectonic Setting ◽  
Source Rocks ◽  
Structural Interpretation ◽  
Resistivity Logs ◽  
Seismic Sections ◽  
Niger Delta Basin ◽  
Structural Closure

Seismic Structural interpretation of subsurface system is a vital tool in mapping source rocks and good trapping system which enhances good understanding of the subsurface system for productive drilling operation. This study is geared towards mapping the structural traps available within the hydrocarbon bearing zones of the “High field” with the use of well log and 3D seismic data. Seven horizons (H1, H2, H3, H4, H5, H6 and H7) were identified on well logs using gamma ray log and resistivity logs. Nine (9) faults were mapped on seismic sections across the field, two (2) of which are major growth faults (F1 and F2), two (2) synthetic faults (F3 and F7) and five (5) antithetic faults (F4, F5, F6, F8 and F9). Rollover anticlines which are structural closure and displayed on the depth structural maps suggest probable hydrocarbon accumulation at the down throw side of the fault F1. Structural interpretation of high field has revealed a highly fault assisted reservoir which depicts the tectonic setting of Niger Delta basin.

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 Methodology and algorithm to correct the thermal neutron porosity for the effect of rare elements and rock minerals with high neutron absorption probability

2021 ◽  
Author(s):  
Moustafa Oraby
Keyword(s):  
Thermal Neutron ◽  
Gamma Ray ◽  
Neutron Transport ◽  
Thermal Neutrons ◽  
Neutron Absorption ◽  
Transport Parameters ◽  
Rare Element ◽  
Absorption Probability ◽  
Rare Elements ◽  
Neutron Porosity

AbstractThe thermal neutron porosity is routinely acquired in almost every well. When combined with the density, gamma ray and resistivity logs, the basic petrophysical parameters of a reservoir are evaluated. The design of the thermal neutron tool is simple, but its interpretation is complex and affected by the formation constituents. The most challenging situation occurs when the formation contains elements with high absorption probability of the thermal neutrons. The existence of such elements changes the neutron transport parameters and results in a false increase in the measured porosity. The problem is reported by many users throughout the years. In 1993, higher thermal neutron porosity is reported due to the existence of an iron-rich mineral, Siderite, in the Nazzazat and Baharia formations in Egypt. Siderite and all iron-rich minerals have high thermal neutrons absorption probability. Recently, in 2018, high thermal neutron porosity in Unayzah field in Saudi Arabia is also reported due to the existence of few parts per million of gadolinium. Gadolinium is a rare element that has high probability of thermal neutron absorption. Currently, none of the existing commercial petrophysics software(s) have modules to correct the thermal neutron porosity for such effects. This represents a challenge to the petrophysicists to properly calculate the actual reservoir porosity. In this paper, the effects of the rare elements and other minerals with high thermal neutron absorption probability on the thermal neutron porosity are discussed, and a correction methodology is developed and tested. The methodology is based on integrating the tool design and the physics of the neutron transport to perform the correction. The details of the correction steps and the correction algorithm are included, tested and applied in two fields.

scholarly journals The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data

2021 ◽  
Vol 10 (1) ◽  
pp. 9-17
Author(s):  
Sudarmaji Saroji ◽  
Ekrar Winata ◽  
Putra Pratama Wahyu Hidayat ◽  
Suryo Prakoso ◽  
Firman Herdiansyah
Keyword(s):  
Machine Learning ◽  
Gamma Ray ◽  
Distribution Patterns ◽  
Effective Porosity ◽  
Depositional Environments ◽  
Beach Sand ◽  
Support Vector ◽  
Data Sets ◽  
Neutron Porosity ◽  
Lithofacies Classification

Lithofacies classification is a process to identify rock lithology by indirect measurements. Usually, the classification is processed manually by an experienced geoscientist. This research presents an automated lithofacies classification using a machine learning method to increase computational power in shortening the lithofacies classification process's time consumption. The support vector machine (SVM) algorithm has been applied successfully to the Damar field, Indonesia. The machine learning input is various well-log data sets, e.g., gamma-ray, density, resistivity, neutron porosity, and effective porosity. Machine learning can classify seven lithofacies and depositional environments, including channel, bar sand, beach sand, carbonate, volcanic, and shale. The classification accuracy in the verification phase with trained lithofacies class data reached more than 90%, while the accuracy in the validation phase with beyond trained data reached 65%. The classified lithofacies then can be used as the input for describing lateral and vertical rock distribution patterns.

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