Review of Case Histories of Two Shallow-Gas Reservoirs in China

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.

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Chalupki Debnianskie Field – Improving Drilling Success in Shallow Gas Reservoirs with VectorSeis®

10.3997/2214-4609-pdb.6.c35 ◽

2003 ◽

Author(s):

D.J. Tessman ◽

E. Gruszczyk ◽

Z. Trzesniowski ◽

P. Misiaczek ◽

P. Brettwood

Keyword(s):

Gas Reservoirs ◽

Shallow Gas

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Development of New Sand Consolidation Fluid and Field Application in Shallow Gas Reservoirs

10.2118/145409-ms ◽

2011 ◽

Cited By ~ 3

Author(s):

Michael James Fuller ◽

Ricardo Andres Gomez ◽

Joel Gill ◽

Cesar Roberto Guimaraes De Carvalho ◽

Ardestya Ferta Abdurachman ◽

...

Keyword(s):

Field Application ◽

Gas Reservoirs ◽

Shallow Gas

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Pliocene Facies Trends and Controls on Deposition of Lower Gusher Shallow Gas Reservoirs, North Coles Levee Field, San Joaquin Basin, California: ABSTRACT

AAPG Bulletin ◽

10.1306/a25fe333-171b-11d7-8645000102c1865d ◽

1994 ◽

Vol 78 ◽

Author(s):

Daniel C. Steward, Janice M. Gilles

Keyword(s):

Gas Reservoirs ◽

Shallow Gas ◽

San Joaquin Basin

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Development Evaluations of Lenticular Shallow Gas Reservoirs with Bottom Water in Shengli Oilfields

10.2118/85768-ms ◽

2003 ◽

Author(s):

Ruyan Sheng ◽

Chuanzhi Cui ◽

Liangcheng Ding

Keyword(s):

Bottom Water ◽

Gas Reservoirs ◽

Shallow Gas

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

10.29118/ipa21-g-220 ◽

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.

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