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All Journal JOURNAL OF EARTH ENERGY ENGINEERING Petro : Jurnal Ilmiah Teknik Perminyakan Scientific Contribution Oil and Gas
Amega Yasutra
Institut TEknologi Bandung
Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Materials Science & Nanotechnology

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A Deliverability Method for Estimating Stabilized Gas Well Performance during Transient Periods on Unconventional Reservoir Amega Yasutra; Calvin Orliando
Journal of Earth Energy Engineering Vol. 10 No. 1 (2021)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2021.5620

Abstract

This study discusses the determination of the stabilized flow coefficient, C, in the Rawlins and Schellhardt equation. It is applicable in the reservoir with low porosity and permeability model, usually found in unconventional reservoirs. In determining the flow coefficient, a deliverability test method proposed by Hashem and Kazemi was used during the transient flow period of a gas well. Besides, in determining the deliverability exponent, n, used in the least squared analysis equation derived by Johnston and Lee in the determination of C stabilized so that from each value of n, there will be supporting data for determining stabilized flow coefficient. Finally, the application and previous method will determine the flow coefficient value based on reservoir model time stabilization. Later it compares with the John Lee equation and IPR constructs from the model and John Lee.
Predicting Stabilized Oil Well Inflow Performance Relationship on Unconventional Reservoir Amega Yasutra; Liviana Purwanto
Journal of Earth Energy Engineering Vol. 10 No. 2 (2021)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2021.5636

Abstract

Unconventional reservoirs are described as any reservoir that requires special recovery operations asides the conventional operating practices. However, low permeability affects the time it requires to attain stability. Presently, most of deliverability test is only carried out in a maximum 24-hour time. Limited test time makes it almost impossible to attain the reservoir stabilization time while carrying out the deliverability test. Meanwhile, to construct Inflow Performance Relationship (IPR) curve, the properties from stabilized time are required. This study aims to discuss how to predict the IPR curve by determining the stabilized flow coefficient value (C) on unconventional reservoir. Furthermore, the stabilized C was used to determine the Inflow Performance Relationship (IPR) for low porosity and permeability reservoir model, also known as Tight Oil Reservoir. The stabilized time and deliverability exponent value need to be determined before the stabilized C value. The stabilized time also know as pseudo-steady state time was evaluated from John Lee and Chaudry equation with validation from the reservoir model. The method proposed by Hashem and Kazemi, which employed the use of transient data in determining the flow coefficient value was also used. In addition, deliverability exponent (n) was determined using an equation proposed by Johnston and Lee. Furthermore, the backpressure equation from Rawlins and Schellhardt was used to construct the IPR curve.
A PROXY MODEL TO PREDICT WATERFLOODING PERFORMANCE IN CHANNELING DELTAIC SAND RESERVOIR Amega Yasutra; Dedy Irawan; Frans Ondihon Sitompul
PETRO: Jurnal Ilmiah Teknik Perminyakan Vol. 9 No. 1 (2020): MARET
Publisher : Jurusan Teknik Perminyakan Fakultas Teknologi Kebumian dan Energi Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1194.198 KB) | DOI: 10.25105/petro.v9i1.5992

Abstract

In recent days, waterflooding activities carried out as a part of secondary recovery. Before performing waterflooding, engineers have to perform reservoir simulation first to predict reservoir performance in order to waterflood. Generally, reservoir simulation is conducted by using numerical simulation method. Numerical simulation gives precise results although it depens on the availiability, quality, and quantity of reservoir characteristic and injection operation data. In addition, numerical simulation also time-consuming and quite complex to use. Proxy model is kind of machine learning. It’s able to predict performance of waterflooding quickly and easier to use. The result isn’t differ too much with numerical simulation method. Proxy model is an equation model that construct form quite many experiment data. This research is trying to predict performance of normal 5 spot waterflooding in reservoir with channeling deltaic sand sedimentation by using proxy model. The proxy model will be tested on a real field case. The results indicate that proxy model is able, faster, reliable and easy to use to predict waterflooding performance in such type of reservoir.
Maximum Allowable Annular Surface Pressure (MAASP) Standards Calculations Study; a Field Case Study Amega Yasutra; Ganesha R Darmawan; Muhammad Rafki
Journal of Earth Energy Engineering Vol. 12 No. 1 (2023)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2023.10047

Abstract

Well integrity failures may arise during the production phase of a well in a field. Those failures could create a Sustained Casing Pressure (SCP), a pressure that is measurable at the wellhead that can not be bled-off. SCP has to be addressed carefully to avoid any uncontrolled fluid flow to other formation or to surface. To maintain SCP value from degrading the other barrier integrity, the pressure threshold should be known and maintained for each annulus in a well. The maximum pressure threshold known as Maximum Allowable Annular Surface Pressure (MAASP). This case study will calculate MAASP from three wells in X field using three known method as outlined in API RP90-2 and ISO 16530-1. API RP 90-2 define two methods in calculation MAASP (known as MAASP – Maximum Allowavle Wellhead Operating Pressure), Simple Derating Method (SDM) and Explicit Derating Method (EDM). The result then compared and evaluted to know the differences, trend of MAASP for each methods, and create a generalization of MAASP/depth for field rule of thumb. For A annulus, the MAASP obtained using API RP90-2 SDM and EDM method is always greater than that obtained using the ISO 16530-1 method. However, for B annulus, the MAASP obtained using the API RP 90-2 SDM method varies, occasionally being greater or less than the ISO 16530-1 method. While in C annulus, the MAASP obtained using the API RP 90-2 SDM and EDM methods is always less than the ISO 16530-1 method. The MAASP/depth generalization will be presented for MAASP ISO 16530-1.
STUDY OF PERMEABILITY PREDICTION USING HYDRAULIC FLOW UNIT (HFU) AND MACHINE LEARNING METHOD IN “BSH” FIELD Babas Samudera Hafwandi; Dedy Irawan; Amega Yasutra
PETRO: Jurnal Ilmiah Teknik Perminyakan Vol. 12 No. 2 (2023): JUNI
Publisher : Jurusan Teknik Perminyakan Fakultas Teknologi Kebumian dan Energi Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/petro.v12i2.15763

Abstract

In this study, Decision Tree, Gradient Boosting, AdaBoost, Random Forest, Support Vector Machines, and K-Nearest Neighbor Machine Learning model are presented that use log and core data available as the basis for permeability prediction. The results were then compared to previously available method, namely Hydraulic Flow Unit (HFU) based on MAE and RMSE Value. The approach was taken by considering correlation relationships between existing log data in predicting permeability values. Three correlations, namely Spearman, Pearson, and Kendall, will be used to determine the relationship between existing log data and permeability. The machine learning model is then compared with the Hydraulic Flow Unit (HFU) Method in predicting the permeability value. The Novelty of this Machine Learning Model is to be able to predict permeability value, to solve the problem of accuracy using the existing method, and to save reasonable time to obtain permeability value by coring in the laboratory by utilizing standard computer available.
PRODUCTION FORECASTING USING ARPS DECLINE CURVE MODEL WITH THE EFFECT OF ARTIFICIAL LIFT INSTALLATION Farrah Maurenza; Amega Yasutra; Iswara Lumban Tungkup
Scientific Contributions Oil and Gas Vol. 46 No. 1 (2023): SCOG
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.46.1.312

Abstract

There are many methods for predicting the production performance of oil wells, using the simplest method by looking at the declining trend of production, such as Decline Curve Analysis (DCA), Material Balanced, or using reservoir simulations. Each of these methods has its advantages and disadvantages. The DCA method, the Arps method, is often used in production forecast analysis to predict production performance and estimate remaining reserves. However, the limitation of this method is that if the production system changes, the trend of decline will also change. At the same time, the application in the field of taking the trend of decreasing production does not pay attention to changes in the production system. This study aims to see that changes in the well production system will affect the downward trend of well production, estimated ultimate recovery (EUR) value, and well lifetime. To see the effect of these changes, the initial data tested used the results of reservoir simulations and field data. From the evaluation results, it is found that if the production system changes during the production time, for example, from changing natural flow using artificial lifting assistance, the trend taken from the production profile will follow the behaviour of the reservoir if the trend is taken in the last system from the production profile, not from the start of production. If the downward trend is taken without regard to the changing system, then the prediction results will not be appropriate
A Case Study of Primary and Secondary Porosity Effect for Permeability Value in Carbonate Reservoir using Differential Effective Medium and Adaptive Neuro-Fuzzy Inference System Method Reza Wardhana; Amega Yasutra; Dedy Irawan; Mochammad Wahdanadi Haidar
Scientific Contributions Oil and Gas Vol. 45 No. 1 (2022): SCOG
Publisher : Testing Center for Oil and Gas LEMIGAS

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Abstract

Pore system in a carbonate reservoir is very complex compared to the pore system in clastic rocks. According to measurements of the velocity propagation of sonic waves in rocks, there are three types of carbonate pore classifi cations: Interpartikel, Vugs and Crack. Due to the complexity of various pore types, errors in reservoir calculation or interpretation might occur. It was making the characterization of the carbonate reservoir more challenging. Differential Effective Medium (DEM) is an elastic modulus modeling method that considers the heterogeneity of pores in the carbonate reservoir. This method adds pore-type inclusions gradually into the host material to the desired proportion of the material. In this research, elastic modulus modeling will be carried out by taking into account the pore complexity of the carbonate reservoir. ANFIS algorithm will also be used in this study to predict the permeability value of the reservoir. Data from well logging measurements will be used as the input, and core data from laboratory will be used as train data to validate prediction results of permeability values in the well depths domain. So, permeability value and pore type variations in the well depth domain will be obtained.
Build of Machine Learning Proxy Model for Prediction of Wax Deposition Rate in Two Phase Flow Water-Oil Jalest Septiano; Amega Yasutra; Silvya Dewi Rahmawati
Scientific Contributions Oil and Gas Vol. 45 No. 1 (2022): SCOG
Publisher : Testing Center for Oil and Gas LEMIGAS

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Abstract

Wax deposit is one of the major fl ow assurance experienced in the process of oil production and transportation from sub- surface to surface. Large amounts of data are required to perform modeling using existing thermodynamic models such as carbon number data from HGTC. In this paper, a machine learning algorithm using unifi ed model approach from Huang (2008). Two types of input are implemented in order to simulate infl uence of feature selection used in training and testing machine learning which are input A consists of water volume fraction (fw), shear stress (τw), effective viscosity (μe), wax concentration gradient (dC/dT), and temperature gradient (dT/dR) and input B consists of water volume fraction (fw), shear stress (τw), effective viscosity (μe), wax concentration gradient (dC/dT), temperature gradient (dT/dR), shear stripping variable (SV) dan diffusion variable (DV). The random forest with Ntree = 500 known to be the best machine learning method compared to others. Based on accuracy parameter it achieves error parameter R-squared (R2) for training, testing and total data for input A and B are 0.999, 0.992, 0.9975 and 0.999, 0.993, 0.9977, respectively.
Prediction of Hydraulic Fractured Well Performance Using Empirical Correlation and Machine Learning Kamal Hamzah; Amega Yasutra; Dedy Irawan
Scientific Contributions Oil and Gas Vol. 44 No. 2 (2021): SCOG
Publisher : Testing Center for Oil and Gas LEMIGAS

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Abstract

Hydraulic fracturing has been established as one of production enhancement methods in the petroleum industry. This method is proven to increase productivity and reserves in low permeability reservoirs, while in medium permeability, it accelerates production without affecting well reserves. However, production result looks scattered and appears to have no direct correlation to individual parameters. It also tend to have a decreasing trend, hence the success ratio needs to be increased. Hydraulic fracturing in the South Sumatra area has been implemented since 2002 and there is plenty of data that can be analyzed to resolve the relationship between actual production with reservoir parameters and fracturing treatment. Empirical correlation approach and machine learning (ML) methods are both used to evaluate this relationship. Concept of Darcy's equation is utilized as basis for the empirical correlation on the actual data. The ML method is then applied to provide better predictions both for production rate and water cut. This method has also been developed to solve data limitations so that the prediction method can be used for all wells. Empirical correlation can gives an R2 of 0.67, while ML can gives a better R2 that is close to 0.80. Furthermore, this prediction method can be used for well candidate selection means.
Feasibility Study and Technical Optimization by Implementing Steam Flooding for the Field Development Plan of A Heavy-Oil Field in Yemen Mohammed Sheikh Salem Al-Attas; Amega Yasutra
Scientific Contributions Oil and Gas Vol. 44 No. 3 (2021): SCOG
Publisher : Testing Center for Oil and Gas LEMIGAS

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Abstract

Enhanced Oil Recovery (EOR) applications are highly recommended and required in Yemen to maintain stable levels of oil production. The field selected for this research is located in Yemen, where relatively- thin sandstone reservoirs are dominant at moderate depths. The reservoir is highly undersaturated with an API gravity of 14.2 and a very low solution gas-oil ratio (GOR), initial oil viscosity (uo) of 420 cP. The reservoir is naturally producing with the support of a strong water drive at the bottom, however, the increase in water cut poses a disadvantage for this reservoir. Over time, the oil production will decline and development plans will be required to improve the oil recovery. This research aims to optimize oil recovery factor and the interest in the overall project economy by evaluating the optimization of the steam flood process based on the Stochastic analysis with the highest recovery factor (RF) and the highest net present value (NPV) objective functions. Two optimization techniques have been used to perform the data analysis, deterministic and stochastic approaches. The deterministic approach is carried out by direct analysis on the results of the technical optimization method using the CMG reservoir simulator, while the stochastic approach uses the simulation results from the deterministic approach to determine the most influencing parameter in the steam flood process as well as to optimize the infill and injection wells location, number of steam injection wells and the steam injection rate with the highest oil RF and highest NPV. In this field development using deterministic approach, two producer wells are converted into injector wells. The RF for this initial scenario is 52,34%, and the NPV is 33.10 MM$/STB. For the second scenario using Stochastic approach, CMOST optimization using the maximum RF objective function resulted in RF of 61.33%, and NPV of 43.00 MMS/STB. Finally for the third scenario using CMOST optimization with the maximum NPV objective function resulted in RF of 57.29%, and an NPV of 53.86 MMS/STB. The Stochastic approach with maximum NPV objective function provides the most favorable scenario to be used in the development of Field "AR". And the optimization using the stochastic approach also produces faster, optimum, and more accurate results than the deterministic approach since it forecast a variety of probable results by running thousands of reservoir simulations using many various estimations of economic conditions.
Co-Authors Babas Samudera Hafwandi Calvin Orliando Dedy Irawan Dedy Irawan Fahrizal Maulana Farrah Maurenza Frans Ondihon Sitompul Ganesha R Darmawan Hamzah, Kamal Irawan, Dedy Iswara Lumban Tungkup Jalest Septiano Jon Felix Turnip Kamal Hamzah Liviana Purwanto Mochammad Wahdanadi Haidar Mohammed Sheikh Salem Al-Attas Muhammad Rafki Reza Wardhana Salem Al-Attas, Mohammed Sheikh Silvya Dewi Rahmawati Sri Dwi Mustaqim