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All Journal Scientific Contributions Oil and Gas LEMBARAN PUBLIKASI MINYAK DAN GAS BUMI (LPMGB)
Humbang Purba
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Chemical Engineering, Chemistry & Bioengineering Energy

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Reducing Residual Moveout Seismic Anisotropy Model Using Three-Ray GMA (General Moveout Approximation) Egie Wijaksono; Humbang Purba
Scientific Contributions Oil and Gas Vol 43 No 3 (2020)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.43.3.553

Abstract

A “ hockey stick” phenomenon is one of anisotropic effects that should be eliminated in marine seismic data. It can increase residual moveout at the far offsets and impact to the distortion of refl ection event amplitude, eventually, reduce the seismic imaging quality. Conventional hyperbolic moveout approximation, an algorithm isotropic model commonly used for seismic processing, has a drawback in supressing such phenomenon. It is also not reliable for medium anisotropy model and long offset data. Many researchers formulated nonhyperbolic moveout approimations but it has limitation analysis for inteval offset-depth ratio (ODR) more than four. We present three-ray generalized moveout approximation (three-ray GMA) for transversely isotropic medium with vertical axis of symmetry (VTI), which is a modifi ed non-hyperbolic moveout approximation from original GMA, to cover up of the weakness of the hyperbolic approximation. The objective of this study is to eliminate “ hockey stick” effect and minimize the residual moveout much smaller at once at the far offsets (offsetdepth ratio > 4). In this study, we used synthetic data for single layer model in VTI medium to calculate relative traveltime error for each recent method over a range of offsets (0 ≤ ODR ≤ 6) and anisotropic parameters (0 ≤  ≤ 0.5). We also make comparative method for multi layer and implement it in a velocity analysis and residual moveout calculation. The three-ray GMA shows a better capability than comparative method to reduce residual moveout for larger offset. This result is important for enhancing seismic imaging.
RESERVOIR CHARACTERIZATION USING SIMULTANEOUS INVERSION TO DELINEATE HYDROCARBON RESERVOIR Muhamad Defi Aryanto; Darsono Darsono; Julikah Julikah; Humbang Purba
Scientific Contributions Oil and Gas Vol 37 No 3 (2014)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.37.3.639

Abstract

Reservoir characterization analysis has been carried out for identifying lithology and Àuid content on TalangAkarFormation in South Sumatra Basin. Robust method that being used in this study is the simultaneous inversion which uses pre-stack gather and well log data from Puja A and Puja B. Elastic parameters resulted from simultaneous inversion are P Impedance, S Impedance and V / V ratio. Lambda-Rho parameter (lr) and Mu-Rho (mr) derived from P impedance (I ) and S impedance (I ). Lambda-Rho is sensitive to Àuid content while Mu-Rho to lithology. Area of interest in this study is Talang Akar Formation as reservoir which contains gas with Lambda-Rho (lr) between 5-15 (GPa*g/cc) and Mu-Rho (mr) 35-45 (GPa*g/cc). The ratio of P wave (Vp) and S wave (Vs) can be used as an indicator to determine Àuid saturation. Gas saturated rock has value of Vp / Vs lower than the water saturated rock. In this study, the ratio of Vp / Vs is 1.5-1.7 for gas saturated rock 
Differenciating Oil, Gas And Water In Seismic Section Using Spectral Decomposition Suprajitno Munadi; Humbang Purba; Rosie A.S
Scientific Contributions Oil and Gas Vol 35 No 2 (2012)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.35.2.780

Abstract

Oil, Gas And Water Contained In The Pore Spaces Should Give Different Response In The Seismic Trace, But This Is Not An Easy Problem. However, From The Physical Point Of View Oil Is Quite Compressible, Gas Is Very Compressible And Water Is Incompressible, So In Principle They Should Give Different Response In Seismic  Record. Since The Response In The Time Domain Is Sometimes Complicated In Nature, An Effort Has Been Carried  Out To Remedy The Problem In The Frequency Domain. A Recent Advance In Signal Analysis Which Is Referred To As The Spectral Decomposition Has Been Used To Differentiate Oil, Gas And Water In Seismic Section. In This Case A Specifi C Method In Spectral Decomposition Called As The Continuous Wavelet Transform (CWT) Was Utilized For This Purpose. The Result Demonstrates That Oil, Gas And Water Can Be Differentiated Clearly In The CWT Spectrum Using The Data From The Deep Water Part In The Makassar Strait. The Results Are Encouraging. 
Spectral Filtering For Removing Coal Bright Spot Effect In Seismic Interpretation Saputro R.A; Suprajitno Munadi; Humbang Purba
Scientific Contributions Oil and Gas Vol 35 No 3 (2012)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.35.3.783

Abstract

Gas Sand And Coal Bed Both Give Bright Appearence In Seismic Section. For The Purpose Of Gas Exploration One Has To Differentiate Between Gas Response And Coal Respone. A Spectral Fi Ltering Technique Is Introduced In This Paper To Solve This Problem. This Technique Is The Development Of The Spectral Decomposition Method Which Constitutes The Leading Edge In Seismic Data Analysis.  Testing Using The Real Seismic Data Has Been Carried Out Using The Seimic Data From West Natuna Basin
Avo Inversion Using Levenberg-Marquardt Optimization Technique Suprajitno Munadi; Humbang Purba
Scientific Contributions Oil and Gas Vol 33 No 2 (2010)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.33.2.812

Abstract

AVO is not only well known as gas indicator over the last two decades but also more importantly, AVO provides us with a means for extracting petrophysical parameters from seismic data. Using AVO anomaly one can derive important petrophysical parameters such as Poisson’s ratio and S-wave velocity. By knowing S-wave velocity nearly all other petrophysical parameters can be calculated. An effective procedure for inverting AVO anomaly is presented in this paper. It avoids inefficient trial and error steps during the matching process between AVO anomaly and calculated AVO. This method uses Levenberg-Marquardt optimization technique.
Spectral Decomposition Made Simple Suprajitno Munadi; Humbang Purba
Scientific Contributions Oil and Gas Vol 32 No 2 (2009)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.32.2.838

Abstract

Spectral decomposition enables the resolution of seismic data to be improved significantly yielding a new possibility to map thin layers such channel sands and any other stratigraphic features. It has also been used in reservoir characterization. There are three methods for implementing spectral decomposition i.e., The Short Time Fourier Transform, the Continuous Wavelet Transform and the Matching Pursuit Decomposition. Among three of them, the Matching Pursuit Decomposition seems to be the most sophisticated one. It gives the best resolution among them. A simple and logical approach for explaining the spectral decomposition methods together with real data examples are presented in this paper by avoiding complex mathematical formulation.
DETERMINATION OF SHALE GAS POTENTIAL OF NORTH SUMATRA BASIN: AN INTEGRATION OF GEOLOGY, GEOCHEMISTRY, PETROPHYSICS AND GEOPHYSICS ANALYSIS Junita Trivianty Musu; Bambang Widarsono; Andi Ruswandi; Himawan Sutanto; Humbang Purba
Scientific Contributions Oil and Gas Vol 38 No 3 (2015)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.38.3.946

Abstract

A detailed combined geological and geophysical study in North Sumatra basin has shown thatprospective formations for shale play containing gas sweet spots are found to be in shales from Bampo,Belumai, and Baong Formations. Bampo Formation exhibits low shale gas potential with very low tomedium in organic material contents, maturity index of immature to mature, and moderate brittleness. Rockswithin the formation tend to be reactive to highly reactive to water, with a moderate degree of swellingcapacity. Porosity varies within 5.8 - 7.4 % with permeability ranging from 0.37 to 3.2 mD. Sweet spots inthe formation found around Basilam-1 and Securai-1wells occupy about 21% of the formation. On the otherhand, Belumai Formation shows moderate to good shale gas potential, with low to high organic materialcontents, immature to mature levels of maturity, and moderately brittle to brittle. Sweet spot areas in theformation found around the two wells are about 29% of the formation. For Baong Formation, analysisreveals moderate to good shale gas potential, with low to medium contents of organic material, immatureto mature in maturity index, moderately brittle to brittle in brittleness, and tendency of being reactiveto highly reactive to water but with low degree of swelling capacity. Sweet spots in the formation foundaround the two wells occupies are roughly 11% of the total formation volume in the area. Basin modelingleading to gas resources estimation for Baong, Belumai and Bampo Formations has led to estimatedvolumes of 6,379 TCF, 16,994 TCF, and 25,024 TCF, respectively, with a total amount of 48,397 TCF.The resources figures are speculative in nature and do not incorporate any certainty and efficiency factors.
PREDIKSI KECEPATAN GELOMBANG S DENGAN MACHINE LEARNING PADA SUMUR “S-1”, CEKUNGAN SUMATERA TENGAH, INDONESIA Sthevanie Dhita Sudrazat; Humbang Purba; Egie Wijaksono; Waskito Pranowo; Muhammad Irsyad Hibatullah
Lembaran publikasi minyak dan gas bumi Vol 54 No 1 (2020)
Publisher : BBPMGB LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/LPMGB.54.1.502

Abstract

Data kecepatan gelombang S (shear) sangat diperlukan untuk karakterisasi reservoar dalam menentukan zona reservoar. Namun data kecepatan gelombang S sangat terbatas dan tersedia pada sumur tertentu saja. Penelitian ini dilakukan untuk memprediksi nilai kecepatan gelombang S dengan menggunakan metode supervised machine learning pada sumur S-1 lapangan migas di cekungan Sumatra Tengah. Simulasi algoritma machine learning dilakukan melalui tahapan sebelum dan setelah tuning pada algoritma library Scikit learn dan algoritma artificial neural network (ANN). Selain itu, parameter dan jumlah data yang digunakan dalam memprediksi nilai kecepatan gelombang akan menentukan nilai error dan akurasi. Hasil analisis menunjukkan bahwa algoritma yang digunakan untuk memperoleh akurasi terbaik pertama dalam memprediksi kecepatan gelombang S, yaitu random forest dengan nilai parameter n_estimator terbaik 10 dan algoritma kedua yang terbaik yaitu k-nearest neighbor dengan nilai parameter n_neighbor terbaik 5.
Algoritma Komputasi Machine Learning untuk Aplikasi Prediksi Nilai Total Organic Carbon (TOC) Sanggeni Gali Wardhana; Henry Julois Pakpahan; Krisdanyolan Simarmata; Waskito Pranowo; Humbang Purba
Lembaran publikasi minyak dan gas bumi Vol 55 No 2 (2021)
Publisher : BBPMGB LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/LPMGB.55.2.606

Abstract

Total Organic Carbon (TOC) merupakan salah satu parameter penting yang digunakan untuk mengevaluasi kemampuan source rock secara kuantitas. Pada umumnya, data TOC diperoleh melalui core yang kemudian dilakukan proses pirolisis rock-eval pada setiap perconto. Namun, proses tersebut memerlukan waku yang cukup lama dan biaya yang cukup besar sehingga data yang didapatkan jumlahnya terbatas. Hal ini akan berimplikasi terhadap validitas penyebaran nilai TOC pada tahapan eksplorasi batuan induk unkonvensional. Data yang terbatas dapat diprediksi dengan pendekatan pola karakterisitik data itu sendiri. Penelitian ini dilakukan bertujuan untuk melakukan prediksi nilai TOC dengan menggunakan algoritma machine learning yaitu Artificial Neural Network, K-Nearest Neighbors, Support Vector Regression, Decision Tree, dan Random Forest dengan memanfaatkan data sumur “A” untuk membangun model dari setiap algoritma machine learning dan data sumur “B” untuk mengevaluasi model yang telah dibangun berdasarkan data sumur “A”. Pengolahan data untuk memprediksi nilai TOC dimulai dari mempersiapkan data pada sumur “A” berdasarkan korelasi yang tinggi pada prediktor dan data output yang akan diprediksi. Selanjutnya dilakukan pembagian atau splitting datasets dengan presentase 60% data digunakan untuk melakukan training dan 40% data sebagai test datasets. Setelah itu, train datasets dapat digunakan untuk membangun model algoritma machine learning. Kemudian dilakukan hyperparameter tuning dan cross validation sehingga dapat dihasilkan model algoritma machine learning dengan hyperparameter tertentu dengan hasil prediksi yang konsisten. Model terbaik diperoleh berdasarkan hasil cross validation dengan menggunakan prediktor dari test datasets hasil splitting sumur “A” dan test datasets dari sumur baru “B”. Hasil penelitian menunjukan bahwa hasil prediksi TOC terbaik pada data sumur “A” diperoleh dengan menggunaan algoritma Random Forest dan pada sumur “B” menggunakan algoritma K-Nearest Neighbors.
Identifi kasi Struktur Bawah Permukaan Lapangan Banyuasin dengan Menggunakan Pemodelan 2.5D Data Gravitasi dan Implikasinya Terhadap Petroleum System Akbar Ramadhani; Edi Sanjaya; Humbang Purba
Lembaran publikasi minyak dan gas bumi Vol 56 No 1 (2022)
Publisher : BBPMGB LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/LPMGB.56.1.919

Abstract

Lapangan migas Banyuasin bagian utara ditemukan adanya indikasi hidrokarbon di beberapa sumur eksplorasi. Namun, masih belum diketahui bentuk lapisan bawah permukaan, konfi gurasi, dan lokasi tempat batuan sumbernya karena terbatasnya data seismik bawah permukaan. Penelitian ini dilakukan untuk menentukan gambaran bawah permukaan, konfi gurasi, dan lokasi cekungan serta petroleum system nya berdasarkan pemodelan 2.5 D data gravity yang diintegrasikan dengan data seismik. Pemodelan dilakukan dengan metode forward modeling gravity dengan memanfaatkan nilai densitas yang berasal dari data sumur. Berdasarkan hasil interpretasi seismik, log sumur, dan forward modelling gravity, struktur bawah pemukaan daerah penelitian tersusun atas lapisan batuan dasar dengan densitas rata-rata 2.9 gr/cc, lapisan batu serpih Lemat berumur Oligocene dengan densitas rata-rata 2.5 gr/cc, lapisan batupasir Talang Akar berumur sekitaran upper oligocene ke lower Miocene dengan densitas rata-rata 2.4 gr/ cc, lapisan karbonat Baturaja berumur lower Miocene dengan densitas rata-rata 2.6 gr/ cc, lapisan batu serpih telisa berumur middle Miocene dengan densitas rata-rata 2.3 gr/cc, serta lapisan sedimen berumur upper Miocen keatas dengan densitas rata-rata 2-2.3 gr/ cc. Lokasi cekungan berada di sebelah selatan dari lokasi sumur eksplorasi. Hasil analisis petroleum system menunjukkan bahwa hidrokarbon yang berasal dari source rock formasi Lemat bermigrasi kearah reservoir batupasir formasi Talang Akar dan formasi Telisa.
Co-Authors Akbar Ramadhani Andi Ruswandi Argya H. Basundara Bagus D. Prasetyo Bambang Widarsono DARSONO Darsono Darsono Edi Sanjaya Egie Wijaksono Henry Julois Pakpahan Himawan Sutanto Julikah Julikah Julikah Junita Trivianty Musu Krisdanyolan Simarmata Muchammad Fariz Muhamad Defi Aryanto Muhammad Irsyad Hibatullah Pradityo Riyadi Ricky A. Tampubolon Rosie A.S Rosie A.S. Sanggeni Gali Wardhana Saputro R.A Saputro, R. A Shidqi A Diria Sthevanie Dhita Sudrazat Suprajitno Munadi Tati Zera Waskito Pranowo Waskito Pranowo