Pore Pressure Prediction using Seismic and Well Data with Eaton Method and Neural Network in Carbonate Reservoir, “P” Gas Field, North Sumatra

2018 ◽  
Author(s):  
P. S. Hutomo
Keyword(s):  
Neural Network ◽  
Pore Pressure ◽  
Carbonate Reservoir ◽  
Gas Field ◽  
Pore Pressure Prediction ◽  
Well Data ◽  
North Sumatra

Pore Pressure Prediction in Unconventional Carbonate Reservoir

2018 ◽  
Author(s):  
M. Elmahdy ◽  
A. E. Farag ◽  
E. Tarabees ◽  
A. Bakr
Keyword(s):  
Pore Pressure ◽  
Carbonate Reservoir ◽  
Pore Pressure Prediction

Seismic pore pressure prediction without well data and poorly defined normal compaction trend lines: a case study from the Río Bravo Delta, Northern Mexico

2005 ◽  
Author(s):  
Juan C. Clarembaux ◽  
Marcelo Giusso ◽  
Roberto Gullco ◽  
Daniel Mujica ◽  
Carlos Carabeo Miranda ◽  
...  
Keyword(s):  
Pore Pressure ◽  
Northern Mexico ◽  
Pore Pressure Prediction ◽  
Rio Bravo ◽  
Well Data

scholarly journals Pore Pressure Prediction Using Artificial Neural Network Based On Logging Data

2020 ◽  
Vol 4 (1) ◽  
pp. 33
Author(s):  
RAKA SUDIRA WARDANA ◽  
Meredita Susanty ◽  
Hapsoro B.W
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pore Pressure ◽  
Direct Measurement ◽  
Empirical Models ◽  
Measurement Methods ◽  
Pressure Data ◽  
Pore Pressure Prediction ◽  
Drilling Operations ◽  
Artificial Neural

Pore pressure is a critical parameter in designing drilling operations. Inaccurate pore pressure data can cause problems, even incidents in drilling operations. Pore pressure data can be obtained from direct measurement methods or estimated using indirect measurement methods such as empirical models. In the oil and gas industry, most of the time, direct measurement is only taken in certain depth due to relatively high costs. Hence, empirical models are commonly used to fill in the gap. However, most of the empirical models highly depend on specific basins or types of formation. Furthermore, to predict pore pressure using empirical models accurately requires a good understanding in determining Normal Compaction Trendline. This proposed approach aims to find a more straightforward yet accurate method to predict pore pressure. Using Artificial Neural Network Model as an alternative method for pore pressure prediction based on logging data such as gamma-ray, density, and sonic log, the result shows a promising accuracy.

Predrill pore‐pressure prediction using seismic data

Geophysics ◽  
2002 ◽  
Vol 67 (4) ◽  
pp. 1286-1292 ◽  
Author(s):  
C. M. Sayers ◽  
G. M. Johnson ◽  
G. Denyer
Keyword(s):  
Velocity Field ◽  
Gulf Of Mexico ◽  
Pore Pressure ◽  
Seismic Data ◽  
Seismic Velocities ◽  
Pressure Data ◽  
Pore Pressure Prediction ◽  
Well Data ◽  
Interval Velocities ◽  
Reflection Tomography

1A predrill estimate of pore pressure can be obtained from seismic velocities using a velocity‐to–pore‐pressure transform, but the seismic velocities need to be derived using methods having sufficient resolution for well planning purposes. For a deepwater Gulf of Mexico example, significant differences are found between the velocity field obtained using reflection tomography and that obtained using a conventional method based on the Dix equation. These lead to significant differences in the predicted pore pressure. Parameters in the velocity‐to–pore‐pressure transform are estimated using seismic interval velocities and pressure data from nearby calibration wells. The uncertainty in the pore pressure prediction is analyzed by examining the spread in the predicted pore pressure obtained using parameter combinations which sample the region of parameter space consistent with the available well data. If calibration wells are not available, the ideas proposed in this paper can be used with measurements made while drilling to predict pore pressure ahead of the bit based on seismic velocities.

scholarly journals Initial pore pressures under the Lusi mud volcano, Indonesia

2015 ◽  
Vol 3 (1) ◽  
pp. SE33-SE49 ◽  
Author(s):  
Mark Tingay
Keyword(s):  
Pore Pressure ◽  
Mud Volcano ◽  
Prediction Methods ◽  
Pressure Gradients ◽  
Modern Times ◽  
Fine Grained ◽  
High Magnitude ◽  
Pore Pressures ◽  
Pore Pressure Prediction ◽  
Well Data

The Lusi mud volcano of East Java, Indonesia, remains one of the most unusual geologic disasters of modern times. Since its sudden birth in 2006, Lusi has erupted continuously, expelling more than 90 million cubic meters of mud that has displaced approximately 40,000 people. This study undertakes the first detailed analysis of the pore pressures immediately prior to the Lusi mud volcano eruption by compiling data from the adjacent (150 m away) Banjar Panji-1 wellbore and undertaking pore pressure prediction from carefully compiled petrophysical data. Wellbore fluid influxes indicate that sequences under Lusi are overpressured from only 350 m depth and follow an approximately lithostat-parallel pore pressure increase through Pleistocene clastic sequences (to 1870 m depth) with pore pressure gradients up to [Formula: see text]. Most unusually, fluid influxes, a major kick, connection gases, elevated background gases, and offset well data confirm that high-magnitude overpressures also exist in the Plio-Pleistocene volcanic sequences (1870 to approximately 2833 m depth) and Miocene (Tuban Formation) carbonates, with pore pressure gradients of [Formula: see text]. The varying geology under the Lusi mud volcano poses a number of challenges for determining overpressure origin and undertaking pore pressure prediction. Overpressures in the fine-grained and rapidly deposited Pleistocene clastics have a petrophysical signature typical of disequilibrium compaction and can be reliably predicted from sonic, resistivity, and drilling exponent data. However, it is difficult to establish the overpressure origin in the low-porosity volcanic sequences and Miocene carbonates. Similarly, the volcanics do not have any clear porosity anomaly, and thus pore pressures in these sequences are greatly underestimated by standard prediction methods. The analysis of preeruption pore pressures underneath the Lusi mud volcano is important for understanding the mechanics, triggering, and longevity of the eruption, as well as providing a valuable example of the unknowns and challenges associated with overpressures in nonclastic rocks.

scholarly journals Pore Pressure Prediction using Well-Logging Data in the West Baram Delta, Offshore Sarawak Basin, Malaysia

2019 ◽  
Vol 8 (4) ◽  
pp. 9172-9178
Keyword(s):  
Pore Pressure ◽  
Transit Time ◽  
Oil Recovery ◽  
Oil And Gas ◽  
Gamma Ray ◽  
Reservoir Modeling ◽  
Gas Field ◽  
Overburden Pressure ◽  
Trend Line ◽  
Pore Pressure Prediction

Well-predicted pore pressure is vital throughout the lifetime of an oil and gas field starting from exploration to the production stage. Here, we studied a mature field where enhanced oil recovery is of high interest and pore pressure data is crucial. Moreover, the top of the overpressure zone in west Baram Delta starts at different depths. Hence, valid pore pressure prediction prior to drilling is a prerequisite for reducing drilling risks, increasing efficient reservoir modeling and optimizing costs. Petrophysical logs such as gamma-ray, density logs, and sonic transit time were used for pore pressure prediction in the studied field. Density logs were used to predict the overburden pressure, whereas sonic transit time, and gamma-ray logs were utilized to develop observed shale compaction trend line (OSCTL) and to establish a normal compaction trend line (NCTL). Pore pressure was predicted from a locally observed shale compaction trend line of 6 wells using Eaton’s and Miller's methods. The predicted pore pressure using Eaton’s DT method with Eaton’s exponent 3 showed a better matching with the measured pressure acquired from the repeat formation test (RFT). Hence, Eaton’s DT method with Eaton exponent 3 could be applied to predict pore pressure for drilling sites in the study area and vicinity fields with similar geological settings.

Depositional Environment And Vertical Distribution Based on Core Analysis to Evaluate The Production Rate of The Reservoir (Case Study: Arun Carbonate Reservoir, North Sumatra, Indonesia)

2020 ◽  
Author(s):  
M., W. Perdana
Keyword(s):  
Production Rate ◽  
Depositional Environment ◽  
Coastal Region ◽  
Secondary Data ◽  
Gas Production ◽  
Carbonate Reservoir ◽  
Petrographic Analysis ◽  
Gas Field ◽  
The North ◽  
North Sumatra

The Arun natural gas field is one of the world biggest gas fields by volume of reserves and this field was found in a coastal region within the North Sumatra Basin, Indonesia. This field has been producing gas and condensate since 1977. Based on Atmadibrata and Joenoes (1993) gas production in this field had increased at that time up to an average level of 3,300 million standard cubic feet per day (MMSCFD). The total daily volume of 2.230 MMSCFD was transported to the LNG plant for liquefaction and trade, 145 MMSCFD was provided to the household showcase and domestic market, 55 MMSCFD was utilized for fuel and 870 MMSCFD was reinjected into the reservoir in order to maximize condensate recovery. This study intends to understand the relationship between the depositional environment of the Arun field reservoir to the production rate of the reservoir. The data in this study is mainly secondary data and consists of geological properties (core description and petrographic analysis), geophysical properties (well logging) and petrophysical properties (porosity and permeability).

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