An Integrated Pore-Pressure Prediction Approach: A Success Strategy to Develop Deep Gas Reservoirs by Drilling through a Highly Depleted Shallow Zone, Semberah Field, East Kalimantan, Indonesia

2011 ◽  
Author(s):  
Usman Jauhari ◽  
Slamet ST ◽  
Iranta Bona Sinaga ◽  
Dayu Agung Anggraini ◽  
Bhakti Widiastuti
Keyword(s):  
Pore Pressure ◽  
Gas Reservoirs ◽  
East Kalimantan ◽  
Pore Pressure Prediction ◽  
Shallow Zone ◽  
Prediction Approach

Machine Learning Technology to Improve Precision and Accelerate Screening Shallow Gas Potentials in Tunu Shallow Gas Zone, PT Pertamina Hulu Mahakam

2021 ◽  
Author(s):  
R. Herbet
Keyword(s):  
Machine Learning ◽  
Learning Technology ◽  
Gas Reservoirs ◽  
Shallow Gas ◽  
Gas Field ◽  
East Kalimantan ◽  
Main Challenge ◽  
Shallow Zone ◽  
Learning Probability ◽  
Mahakam Delta

Tunu is a giant gas field located in the present-day Mahakam Delta, East Kalimantan, Indonesia. Tunu gas produced from Tunu Main Zone (TMZ), between 2500-4500 m TVDSS and Tunu Shallow Zone (TSZ) located on depth 600 - 1500 m TVDSS. Gas reservoirs are scattered along the Tunu Field and corresponds with fluio-deltaic series. Main lithologies are shale, sand, and coal layers. Shallow gas trapping system is a combination of stratigraphic features, and geological structures. The TSZ development relies heavily on the use seismic to assess and identify gas sand reservoirs as drilling targets. The main challenge for conventional use of seismic is differentiating the gas sands from the coal layers. Gas sands are identified by an established seismic workflow that comprises of four different analysis on pre-stack and angle stacks, CDP gathers, amplitude versus angle(AVA), and inversion/litho-seismic cube. This workflow has a high success rate in identifying gas, but requires a lot of time to assess the prospect. The challenge is to assess more than 20,000 shallow objects in TSZ, it is important to have a faster and more efficient workflow to speed up the development phase. The aim of this study is to evaluate the robustness of machine learning to quantify seismic objects/geobodies to be gas reservoirs. We tested various machine learning methods to fit learn geological Tunu characteristic to the seismic data. The training result shows that a gas sand geobody can be predicted using combination of AVA gather, sub-stacks and seismic attributes with model precision of 80%. Two blind wells tests showed precision more than 95% while other final set tests are under evaluated. Detectability here is the ability of machine learning to predicted the actual gas reservoir as compared to the number of gas reservoirs found in that particular wells test. Outcome from this study is expected to accelerate gas assessment workflow in the near future using the machine learning probability cube, with more optimized and quantitative workflow by showing its predictive value in each anomaly.

scholarly journals Logging Evaluation Method for Pore Pressure of Shale Gas Reservoirs- Taking Fuling Area, Sichuan Basin as an Example

2015 ◽  
Vol 8 (1) ◽  
pp. 350-353
Author(s):  
Hao Shibo ◽  
Jin Wujun
Keyword(s):  
Pore Pressure ◽  
Shale Gas ◽  
Evaluation Method ◽  
Oil And Gas ◽  
Gas Reservoirs ◽  
Shale Gas Reservoirs ◽  
Oil And Gas Resources ◽  
Pore Pressure Prediction ◽  
Formation Pore Pressure ◽  
Exploration And Development

Unconventional oil and gas resources, especially shale gas resources have great potential for exploration and development in China. In Shale gas exploration and development process, reservoir pore pressure is a very important parameter, and the pore pressure prediction can improve the appraisal accuracy of project dessert. This paper analyzes several formation pore pressure calculation methods based on logging data, and optimized the “Equivalent depth method”. The preliminary results show that this method can carry out the accurate evaluation on the abnormal high pressure of the shale gas reservoirs of Fuling area with high calculation precision and application potential.

Combining porosity and resistivity logs for pore pressure prediction

2021 ◽  
pp. 108819
Author(s):  
Augustine Uhunoma Osarogiagbon ◽  
Olalere Oloruntobi ◽  
Faisal Khan ◽  
Ramachandran Venkatesan ◽  
Paul Gillard
Keyword(s):  
Pore Pressure ◽  
Resistivity Logs ◽  
Pore Pressure Prediction

Seismic pore pressure prediction with uncertainty using a probabilistic mechanical earth model

2003 ◽  
Author(s):  
P. M. Doyen ◽  
A. Malinverno ◽  
R. Prioul ◽  
P. Hooyman ◽  
S. Noeth ◽  
...  
Keyword(s):  
Pore Pressure ◽  
Earth Model ◽  
Pore Pressure Prediction ◽  
Mechanical Earth Model
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