Optimasi Volume Injeksi Pada Waterflooding Menggunakan Metode Artificial Neural Network

Rizka Haswinda Putri, Tomi Erfando

Abstract


Waterflooding adalah salah satu metode pemulihan sekunder yang bertujuan untuk mempertahakan tekanan reservoir. Volume air injeksi disesuaikan agar tidak terjadi penurunan oil recovery. Tujuan dari penelitian ini adalah untuk mengoptimalkan nilai Injection Volume dan Recovery Factor (RF) dengan menggunakan metode Artificial Neural Network (ANN). Parameter yang digunakan adalah porositas, permeabilitas horizontal, permeabilitas vertikal, saturasi minyak, saturasi air, kompresibilitas batuan. Software simulasi reservoir menggunakan Computer Modeling Group (CMG), kemudian optimasi menggunakan Machine Learning (ML). Pendekatan Machine Learning menggunakan rasio 0,7:0,3 untuk data pelatihan dan pengujian. kemudian dilakukan trial and error pada 10 node hidden layer. Hasil penelitian memiliki akurasi yang tinggi karena nilai R2 pada data training dan testing mendekati 1, sehingga nilai optimasi pada recovery factor sebesar 26.17%, meningkat sebesar 5.85% dari basecase dan volume injeksi sebesar 15387684 bbl atau 15.4 MMbbl.

Keywords


waterflooding, recovery factor, volume injeksi, artificial neural network, backpropagtion algorithm

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References


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DOI: https://doi.org/10.32672/jse.v8i2.5987

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