The Seismic Exploration of the Mahakam Delta, or "Nine Years Shooting in Rivers, Swamps and Very Shallow Offshore"

Tambora field is a mature gas field located in a swamp area of Mahakam delta without artificial lift. The main objective of this project is to unlock existing oil resources. Most oil wells could not flow because there is no artificial lift, moreover the network pressure is still at Medium Pressure (20 Barg). Given the significant stakes, the option to operate the testing barge continuously as lifting tool is reviewed. The idea is to set the separator pressure to 1-3 Barg, so that the wellhead flowing pressure could be reduced to more than 15 Barg which will create higher drawdown in front of the reservoir. The oil flows from the reservoir into the gauge tank, where it is then returned to the production line by transfer pumps. The trial was performed in well T-1 for a week in November 2017 and successfully produced continuous oil with a stable rate of 1000 bbls/d. What makes this project unique is the continuous operation for a long period of time. Therefore, it is important to ensure the capacity of the gauge tank and the transfer pump compatibility with the rate from the well, the system durability which required routine inspection and maintenance to ensure the testing barge unit is in prime condition and to maintain vigilance and responsiveness of personnel. This project started in 2018 for several wells and the cumulative production up to January 2020 has reached 158 k bbls and will be continued as there are still potential oil resources to be unlocked. Innovation does not need to be rocket science. Significant oil recovery can be achieved with a simple approach considering all safety operation, production and economic aspect.

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Mahakam Field Characterization Using Production Type-Curve With Business Intelligence Application

Proc. Indon. Petrol. Assoc., Digital Technical Conference, 2020 ◽

10.29118/ipa20-e-242 ◽

2020 ◽

Author(s):

P. Noverri

Keyword(s):

Business Intelligence ◽

Data Gathering ◽

Production Data ◽

Type Curve ◽

Type Curves ◽

Production Type ◽

Efficient Data ◽

Production Behavior ◽

Factor Type ◽

Mahakam Delta

Delta Mahakam is a giant hydrocarbon block which is comprised two oil fields and five gas fields. The giant block has been considered mature after production for more than 40 years. More than 2,000 wells have been drilled to optimize hydrocarbon recovery. From those wells, a huge amount of production data is available and documented in a well-structured manner. Gaining insight from this data is highly beneficial to understand fields behavior and their characteristics. The fields production characterization is analyzed with Production Type-Curve method. In this case, type curves were generated from production data ratio such as CGR, WGR and GOR to field recovery factor. Type curve is considered as a simple approach to find patterns and capture a helicopter view from a huge volume of production data. Utilization of business intelligence enables efficient data gathering from different data sources, data preparation and data visualization through dashboards. Each dashboard provides a different perspective which consists of field view, zone view, sector view and POD view. Dashboards allow users to perform comprehensive analysis in describing production behavior. Production type-curve analysis through dashboards show that fields in the Mahakam Delta can be grouped based on their production behavior and effectively provide global field understanding Discovery of production key information from proposed methods can be used as reference for prospective and existing fields development in the Mahakam Delta. This paper demonstrates an example of production type-curve as a simple yet efficient method in characterizing field production behaviors which is realized by a Business Intelligent application

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Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR

Applied Sciences ◽

10.3390/app11115028 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5028

Author(s):

Miaomiao Sun ◽

Zhenchun Li ◽

Yanli Liu ◽

Jiao Wang ◽

Yufei Su

Keyword(s):

Reflection Coefficient ◽

Compressed Sensing ◽

Objective Function ◽

Low Frequency ◽

Original Data ◽

Seismic Exploration ◽

High Signal ◽

Inversion Process ◽

Frequency Information ◽

Low Snr

Low-frequency information can reflect the basic trend of a formation, enhance the accuracy of velocity analysis and improve the imaging accuracy of deep structures in seismic exploration. However, the low-frequency information obtained by the conventional seismic acquisition method is seriously polluted by noise, which will be further lost in processing. Compressed sensing (CS) theory is used to exploit the sparsity of the reflection coefficient in the frequency domain to expand the low-frequency components reasonably, thus improving the data quality. However, the conventional CS method is greatly affected by noise, and the effective expansion of low-frequency information can only be realized in the case of a high signal-to-noise ratio (SNR). In this paper, well information is introduced into the objective function to constrain the inversion process of the estimated reflection coefficient, and then, the low-frequency component of the original data is expanded by extracting the low-frequency information of the reflection coefficient. It has been proved by model tests and actual data processing results that the objective function of estimating the reflection coefficient constrained by well logging data based on CS theory can improve the anti-noise interference ability of the inversion process and expand the low-frequency information well in the case of a low SNR.

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