scholarly journals The Study of Reservoir Zones of Asmari Formation Based on Sequence Stratigraphy and Well-logging Data in a Southwestern Oil Wells of Iran

2016 ◽  
Vol 4 (2) ◽  
pp. 211
Author(s):  
Bahman Kiani Shavandi ◽  
Vahid Ahmadi
Keyword(s):  
Sequence Stratigraphy ◽  
Lower Limit ◽  
Depositional Environment ◽  
Well Logging ◽  
Oil Wells ◽  
Reservoir Rock ◽  
Asmari Formation ◽  
Dolomitic Limestone ◽  
Upper Limit

<div><p><em>Asmari formation in Maroon Oilfield is made up of fossil-bearing limestone, dolomitic limestone, argillaceous limestone, sandstone and shale. It is the main reservoir rock for multiple oil wells of Zagros. The formation in Maroon Oilfield dates back to Oligocene(Rupelian – Burdigalian). In the present study, sequence stratigraphy and depositional environment of Asmari Reservoir Formation located in Maroon Oilfield are studied through well-logging data. The lower limit of Asmari Formation in the studied area is Pabede Formation and its upper limit is Gachsaran Formation. The lower limit between Asmari and Pabede formations and upper limit between Asmari and Gachsaran formations are not continuous and signs of discontinuity are observed in them. The field, macroscopic and microscopic studied and determination of percentage and type of allochem and orthochem elements led to identification of 17 carbonate and clastic facies in Asmari Formation. In the section, 6 depositional sequences were identified and labeled as sequence 1-6. The sequence limits are all of SB2 type with exception of Sequence 6 which is SB1. </em></p></div>

scholarly journals Bio-Sequence Stratigraphy of Asmari Formation in the Southeast of Norabad (Zagros Basin, SW Iran)

2021 ◽  
Author(s):  
Mohammadsadegh Dehghanian
Keyword(s):  
Sea Level ◽  
Sequence Stratigraphy ◽  
Carbonate Platform ◽  
Reservoir Rock ◽  
Dispersion Pattern ◽  
Asmari Formation ◽  
Level Curve ◽  
Zagros Basin ◽  
Lithostratigraphic Units ◽  
Global Sea Level

Abstract Asmari Formation is the thick sequence of carbonate sediment in the range of Oligocene-Miocene which is deposited in the foreland basins of the Zagros and is considered as the original and most famous reservoir rock of Zagros basin. To study of lithostratigraphic units and sequence stratigraphy of this Formation, the section in the southeast of Norabad was selected. Field study indicated that Asmari Formation possessed the thickness of 401.5m and included 9 lithostratigraphic units. According to the study of microfacies, Stacking pattern and identification of main sequence level, three depositional sequences including two-second order and one-third order sequence were recognized. The sea-level curve in the studied section indicated that it was correspondence to the global sea level curve. These facies deposited in five environmental sedimentations as follow Open Sea Shelf (Fore Barrier), Bar, Lagoon, Back bar shelf, and Shoal. The environment is part of a carbonate platform that has been formed on an open shelf. In addition, according to the Study of foraminifer dispersion pattern the range of Asmari Formation in Norabad was suggested to be Oligocene (Rupelian- Chattian) to lower Miocene (Aquitanian- Burdigalian).

Concerning the Techniques for Plugging Coning Fluids in Oil Wells

1990 ◽  
Vol 112 (3) ◽  
pp. 157-162
Author(s):  
D. L. Lekia ◽  
R. M. Knapp ◽  
R. D. Evans
Keyword(s):  
Flow Rate ◽  
Radial Distance ◽  
Oil Wells ◽  
Reservoir Rock ◽  
Fluid Injection ◽  
Permeability Barrier ◽  
Rock Stress ◽  
Fluid Production ◽  
Horizontal Permeability

A technique is presented for the determination of the size of a horizontal permeability barrier (plug) that can be displaced from the wellbore to suppress cone-fluid production in individual oil wells with coning problems. Details are given concerning the computation of the plug size for any well with known wellbore and completion constants. Also discussed are materials which can be used to create the plugs. Based on literature data and rock stress considerations, plugging by fluid injection is found to be much more practical and general in application than methods based upon hydraulic fracturing technology. Two types of plugging fluids are recommended for injection into the formation as a function of pressure and flow rate conditions. The injected plug-fluid penetrates the reservoir rock surrounding the wellbore to the radial distance given by a predetermined radius of the plug. Predictive calculations show that production of cone-fluid-free oil can be enhanced by as much as 100 percent after a successful plugging job is done using the method presented.

Depositional environment and sequence stratigraphy of the Oligo-Miocene Asmari Formation in SW Iran

Facies ◽  
2005 ◽  
Vol 52 (1) ◽  
pp. 41-51 ◽  
Author(s):  
Hossein Vaziri-Moghaddam ◽  
Masoud Kimiagari ◽  
Azizolah Taheri
Keyword(s):  
Sequence Stratigraphy ◽  
Depositional Environment ◽  
Asmari Formation ◽  
Sw Iran

Quantitative Radioisotope Measurement of Duodenogastric Reflux in Patients with Ulcer or Gastrectomized for Ulcer

1985 ◽  
Vol 24 (03) ◽  
pp. 107-110
Author(s):  
M. Pääkkönen ◽  
S. Aukee ◽  
K. Korhonen ◽  
A. Pääkkönen ◽  
E. Länsimies ◽  
...  
Keyword(s):  
Gamma Camera ◽  
Background Activity ◽  
Bile Reflux ◽  
Duodenogastric Reflux ◽  
Upper Limit ◽  
Visual Evidence ◽  
Biochemical Measurement

SummaryIn this work the duodenogastric reflux was quantified as the amount of radioactivity entering the stomach after an i.v. administration of 99mmTc-HIDA in ulcer patients and in patients who had undergone BI gastrectomy. The results were compared with visual evidence of gastric activity in the gamma camera images and biochemical determination of gastric bile reflux. The method is useful in quantifying the reflux if the activity is above the background activity. It allows the determination of an upper limit for the reflux when the reflux is evident visually. Only two or three images are needed for the quantitation. No correlation was found between biochemical measurement of fasting bile reflux in the stomach and radioisotopic quantification.

Determination of reservoir rock residual water using X-ray computed microtomography

2018 ◽  
pp. 38-42 ◽  
Author(s):  
I.V. Yazynina ◽  
◽  
E.V. Shelyago ◽  
A.A. Abrosimov ◽  
N.E. Grachev ◽  
...  
Keyword(s):  
Reservoir Rock ◽  
Residual Water ◽  
X Ray ◽  
Computed Microtomography ◽  
X Ray Computed

PNN and BP Neural Network Fault Diagnosis Research on Electronic Accelerator Pedal Detection

2020 ◽  
Vol 14 (2) ◽  
pp. 205-220
Author(s):  
Yuxiu Jiang ◽  
Xiaohuan Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Lower Limit ◽  
Bp Neural Network ◽  
Accelerator Pedal ◽  
Electronic Throttle ◽  
Measured Voltage ◽  
Upper Limit ◽  
Network Fault

Background: The working state of electronic accelerator pedal directly affects the safety of vehicles and drivers. Effective fault detection and judgment for the working state of the accelerator pedal can prevent accidents. Methods: Aiming at different working conditions of electronic accelerator pedal, this paper used PNN and BP diagnosis model to detect the state of electronic accelerator pedal according to the principle and characteristics of PNN and BP neural network. The fault diagnosis test experiment of electronic accelerator pedal was carried out to get the data acquisition. Results: After the patents for electronic accelerator pedals are queried and used, the first measured voltage, the upper limit of first voltage, the first voltage lower limit, the second measured voltage, the upper limit of second voltage and the second voltage lower limit are tested to build up the data samples. Then the PNN and BP fault diagnosis models of electronic accelerator pedal are established. Six fault samples are defined through the design of electronic accelerator pedal fault classifier and the fault diagnosis processes are executed to test. Conclusion: The fault diagnosis results were analyzed and the comparisons between the PNN and the BP research results show that BP neural network is an effective method for fault detection of electronic throttle pedal, which is obviously superior to PNN neural network based on the experiment data.

Sign in / Sign up

Export Citation Format

Share Document