scholarly journals Exploration history and trapping mechanism of Peciko gas field, East Kalimantan, Indonesia.

1996 ◽  
Vol 61 (1) ◽  
pp. 25-34 ◽  
Author(s):  
Yoshiyuki Hayashi ◽  
Tsuyoshi Inage ◽  
Ikuo Suzuki ◽  
Hiroshi Nagura
Keyword(s):  
Gas Field ◽  
Trapping Mechanism ◽  
East Kalimantan

SANDSTONE RESERVOIR GEOMETRY OF NON-MARINE SEDIMENTS IN THE TOOLACHEE GAS FIELD

1977 ◽  
Vol 17 (1) ◽  
pp. 50
Author(s):  
S. Bevan Devine ◽  
Colin G. Gatehouse
Keyword(s):  
Gas Field ◽  
Cross Sectional ◽  
Field Extensions ◽  
Trapping Mechanism ◽  
Reservoir Geometry ◽  
Structural Closure ◽  
Basin Boundaries ◽  
Field Area ◽  
Reserve Estimates ◽  
Cooper Basin

The concept of Genetic Increments of Strata (GIS) has been applied to correlations of the non-marine rocks of the Early Permian Patchawarra Formation of the Cooper Basin. Boundaries of GIS in the non-marine rocks are related to a dynamic model of non-marine deposition in which discrete sandstone bodies result from channel activity.In the 9 wells in the Toolachee Gas Field area of about 150 sq. miles, log correlations based on these principles determined eight major sandstone bodies, six of which hold gas reserves. The sandstone bodies are elongate and sinuous. They have cross-sectional dimensions of about 5 miles wide by up to 60 ft thick. Faulting and differential compaction have influenced the locations of the axes of the channel sandstone bodies.The value of mapping the geometry of the channel sandstones in the Cooper Basin lies in establishing a possible trapping mechanism which is independent of structural closure and which requires only a structural dip nonperpendicular to the channel sandstone direction; and providing a geologic basis for gas reserve estimates, the positioning of future appraisal and development wells and the prediction of field extensions and nearby new fields.An estimate of the proven-probable gas in place in the Toolachee Gas Field (560 BCF) based on the channel sandstone mapping is comparable with estimates based on lumping all pay intervals together in each well and drawing geometric pay isoliths. Estimates of possible reserves are increased by the mapping because of the introduction of the trapping mechanism of sandstone margins. The Toolachee Field has the potential to be perhaps doubled in size.

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.

Succesful Application of Cluster Drilling Concept to Reduce Cost and Increase Profitability in a Mature Onshore Gas Field in East Kalimantan, Indonesia

2008 ◽  
Author(s):  
Hazman Chatib ◽  
Harry Alam ◽  
Andre Wijanarko ◽  
Umi Kurniyati ◽  
Yoseph Agung ◽  
...  
Keyword(s):  
Gas Field ◽  
East Kalimantan

Shallow Gas Identification using LWD Monopole Sonic: A-non Radioactive for Safe and Effective Logs Acquisition in Tunu Field, Mahakam Delta

2020 ◽  
Author(s):  
A.T. Santoso
Keyword(s):  
Oil And Gas ◽  
Neutron Density ◽  
Shallow Gas ◽  
Gas Field ◽  
Safety Issues ◽  
East Kalimantan ◽  
Main Challenge ◽  
Gas Identification ◽  
Fluid Interpretation ◽  
Mahakam Delta

The Tunu field is a swamp giant gas field located in the Mahakam Delta, East Kalimantan. Stratigraphically, this field has an anticline structure with three main intervals; Tunu Shallow Zone (TSZ), Fresh Water Zone (FWZ), and Tunu Main Zone (TMZ). Shallow gas reservoirs of TSZ have been produced since 2008, following the production of TMZ in the 1990s. Drilling targets in the shallow gas reservoir decreased significantly due to limited reservoir targets, high inclination wells and a low oil price environment. The utilization of radioactive source logging (density and neutron) on Logging While Drilling (LWD) tools is not recommended to be performed in open hole mode for operational and safety issues (e.g: tool stuck). Thus, LWD Monopole sonic is chosen as a replacement of LWD Neutron-Density logs and helps to differentiate between shallow gas potential and coal lithology which is the main challenge in TSZ at interval depth above 1200 mSS. The methodology utilized sonic semblance (STRA) and compressional slowness (DTc) data at real-time and memory data logs, so early decision can be made in drilling mode. In a gas-bearing reservoir, both semblance and slowness are missing, while in coal it produced strong semblance. In order to differentiate carbonate lithology, additional data, such as cutting, calcimetry, drilling Rate of Penetration and Gas While Drilling are utilized. During 2018-2020, 5 wells have been drilled using LWD Monopole sonic together with LWD GR-Resistivity-Neutron-Density (Triple Combo) to calibrate the fluid interpretation and 3 trial wells with only GR-Resistivity-Monopole Sonic. As a result, LWD Monopole sonic is able to differentiate between Gas and Coal based on semblance and slowness with a success ratio up to 80%. This LWD Monopole Sonic provides a non-radioactive solution for safe and effective logs acquisition for shallow gas identification that could be applied in oil and gas fields outside Mahakam.

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