Drilling and Cementing Through Highly Active Shallow Gas Field

2020 ◽  
Author(s):  
Abdulqawi Alfakih ◽  
Amir Galaby ◽  
Robert Famiev ◽  
Nauman Sadiq
Keyword(s):  
Shallow Gas ◽  
Gas Field ◽  
Highly Active

Reserves Determination Using Type Curve Matching and Extended Material Balance Methods in the Medicine Hat Shallow Gas Field

1994 ◽  
Author(s):  
S. L. West ◽  
P. J. R. Cochrane
Keyword(s):  
Material Balance ◽  
Poor Quality ◽  
Curve Matching ◽  
Pressure Data ◽  
Gas Reservoirs ◽  
Shallow Gas ◽  
Gas Field ◽  
Type Curve ◽  
Balance Analysis ◽  
Diffusivity Equation

Tight shallow gas reservoirs in the Western Canada Basin present a number of unique challenges in accurately determining reserves. Traditional methods such as decline analysis and material balance are inaccurate due to the formations' low permeabilities and poor pressure data. The low permeabilities cause long transient periods not easily separable from production decline using conventional decline analysis. The result is lower confidence in selecting the appropriate decline characteristics (exponential or harmonic) which significantly impacts recovery factors and remaining reserves. Limited, poor quality pressure data and commingled production from the three producing zones results in non representative pressure data and hence inaccurate material balance analysis. This paper presents the merit of two new methods of reserve evaluation which address the problems described above for tight shallow gas in the Medicine Hat field. The first method applies type curve matching which combines the analytical pressure solutions of the diffusivity equation (transient) with the empirical decline equation. The second method is an extended material balance which incorporates the gas deliverability theory to allow the selection of appropriate p/z derivatives without relying on pressure data. Excellent results were obtained by applying these two methodologies to ten properties which gather gas from 2300 wells. The two independent techniques resulted in similar production forecasts and reserves, confirming their validity. They proved to be valuable, practical tools in overcoming the various challenges of tight shallow gas and in improving the accuracy in gas reserves determination in the Medicine Hat field.

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.

Coiled-Tubing Fracturing Increases Deliverability, Recoverable Reserves, and NPV of Infill Development Wells in a Mature Shallow Gas Field

2002 ◽  
Author(s):  
Stanley F. Wolny ◽  
Glen Schiffner ◽  
Eric G. Schmelzl ◽  
Merrill Jamieson ◽  
Marty Stromquist
Keyword(s):  
Shallow Gas ◽  
Gas Field ◽  
Infill Development ◽  
Coiled Tubing ◽  
Recoverable Reserves

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.

Applications of a Multizone Single-Trip Gravel-Pack System in Developing a Shallow-Gas Field, Indonesia: Case History

2018 ◽  
Author(s):  
B. H. Muryanto ◽  
W. Fransiskus ◽  
R. Wijaya ◽  
B. Styward ◽  
Y. Ji ◽  
...  
Keyword(s):  
Case History ◽  
Shallow Gas ◽  
Gas Field ◽  
Gravel Pack

scholarly journals Current Status of Shallow Gas Field Development

2018 ◽  
Vol 55 (5) ◽  
pp. 491-499
Author(s):  
Young-Jun Moon ◽  
YoungIl Choi ◽  
Hyo-Jin Shin ◽  
Jong-Se Lim
Keyword(s):  
Current Status ◽  
Shallow Gas ◽  
Gas Field ◽  
Field Development
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