By International Petroleum Technology Conference (IPTC) Monday, 25 March 0900-1600 hours Instructors: Olivier Dubrule and Lukas Mosser, Imperial College London Deep Learning (DL) is already bringing game-changing applications to the petroleum industry, and this is certainly the beginning of an enduring trend. Many petroleum engineers and geoscientists are interested to know more about DL but are not sure where to start. This one-day course aims to provide this introduction. The first half of the course presents the formalism of Logistic Regression, Neural Networks and Convolutional Neural Networks and some of their applications. Much of the standard terminology used in DL applications is also presented. In the afternoon, the online environment associated with DL is discussed, from Python libraries to software repositories, including useful websites and big datasets. The last part of the course is spent discussing the most promising subsurface applications of DL.
Tawekal, Ricky L. (Institut Teknologi Bandung) | Shanti, Parama (Institut Teknologi Bandung) | Kurniawan, Dwinanto B. (PT. Bina Rekacipta Utama ) | Arifin, Muhamad (PT. Pertamina Hulu Energi) | Zen, Doni (PT. Pertamina Hulu Energi) | Haryanto, _ (Ministry of Energy and Mineral Resources Republic of Indonesia) | Gumilang, Fentarie (Ministry of Energy and Mineral Resources Republic of Indonesia) | Banarwoto, _ (Ministry of Energy and Mineral Resources Republic of Indonesia)
Many offshore platforms have been developed in the offshore fields in Indonesia since 1969. National authorities in Indonesia stated that the existing platforms required the performance of underwater inspection on a regular basis. However, underwater inspections are very costly. Hence, this paper proposed Risk-based Underwater Inspection (RBUI) analysis as a highly effective method of conducting underwater inspections. The analytical approach was adopted from the American Petroleum Institute Recommended Practice for Structural Integrity Management of Fixed Offshore Structures in which a mix of quantitative analysis and qualitative analysis methods were used. The analysis generated the Probability of Failure (PoF) and the Consequence of Failure (CoF) that were subsequently required to determine the risk level of a platform. Additionally, the exposure level was also categorized for each platform based on its consequence category and life safety aspects. The usage of a combination of risk levels and exposure categories resulted in different inspection intervals for each platform. In this study, only characteristic factors were used for the PoF calculation. The condition factors were accounted for in the anomaly treatment. Hence, the RBUI analysis for 14 fixed platforms in West Madura Offshore resulted in an inspection plan that accentuated safety, but also had a longer interval when compared with the previous time-based methods.
The first offshore platform in Indonesia was built in 1969. Since then, there was a tremendous increase in the number of offshore platforms. Previously, based on Minister of Mining Regulations no. 05 /P/II/PERTAMBANGAN/1997 (1977), Indonesian authority necessitate regular inspections of all oil and gas offshore platforms in which minor, major, and complete inspections require annual, biennial, and four-year inspection periods, respectively.
The decline in oil prices since 2014 has forced most oil companies to cut costs. Indonesian companies are not an exception to this matter. The implementation of the risk-based underwater inspection (RBUI) is an alternative to traditional cost cutting methods in underwater inspections. Therefore, a new regulation recommends instead of the conventional time-based method, the inspection should be performed by a risk-based method that considers the risk level of the offshore platforms.
To help with this difficulty, a cost-effective method has been proposed to boost the hydrocarbon recovery by optimizing well locations through the Simulated Opportunity Index (SOI). SOI is an intelligent method to identify zones with high potential for production which is empirically calculated from basic rock and fluid properties, and from reservoir pressure as its energy capacity. In order to obtain the best results, the original SOI formula (Molina et al., 2009) was extended to both oil and gas fields. Based on this modified SOI formula, a software program has been developed to locate the best well locations considering multilayer, existing wells, and fault existences. This paper describes how the SOI software helps as a simple, fast, and accurate way to obtain the higher hydrocarbon production than that of trial-error method and previous studies in two different fields located in offshore Indonesia. On one hand, the proposed method could save money by minimizing the required number of wells. On the other hand, it could maximize profit by maximizing recovery.
The study area, Madura Island separated from East Java by Madura Strait, is located in the North-East Java Basin. The early Middle Miocene Ngrayong Formation, an important petroleum reservoir, is well exposed in the central highland area of Madura. It is the product of a wide variety of depositional environments. The purpose of present study is to determine the reservoir quality based on the characteristics and geometry of sedimentary facies and the variation of depositional environments. The Ngrayong Formation in Madura Island can be identified into seven litho-facies and two facies associations. Cross-laminated sandstone facies (Smx) and heterolithic sandstone facies (Sm) are deposited in tide-dominated, littoral to sub-littoral environment. Dark grey siltstone facies (Fc), grey shale facies (Fs), laminated sandstone facies (Sl), fine grained sandstone facies (Sf) and bioclastic limestones facies (Lgb) are deposited in moderate-depth sub-littoral environment. In the study area, the depositional environment of Ngrayong Formation moves to deeper environment from north to south. The reservoir rocks deposited in upper neritic zone such as cross-laminated sandstones and heterolitic sandstones possess good reservoir quality. The reservoir rocks deposited in moderate-depth shallow marine shelf environment are also fair for reservoir potential. The cross-laminated sandstone (Smx) and heterolithic sandstone (Sm) in upper neritic sediments facies association and laminated sandstone (Sl) and fine grained sandstone (Sf) in lower neritic sediments facies association should be targeted for hydrocarbon exploration in Madura Island.
Sanjaya, Sanggum (Halliburton) | Quintero, Luis (Halliburton) | Iyer, M.S. (Halliburton) | Diametrica, Septian (Pertamina Hulu Energi - WMO) | Praveen, Florence (Pertamina Hulu Energi - WMO) | Soendaroe, Achmad (PT Pertamina Hulu Energi WMO) | Harkomoyo, Harkomoyo (PT Pertamina Hulu Energi WMO)
Copyright 2014, Offshore Technology Conference This paper was prepared for presentation at the Offshore Technology Conference Asia held in Kuala Lumpur, Malaysia, 25-28 March 2014. This paper was selected for presentation by an OTC program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Offshore Technology Conference and are subject to correction by the author(s). The material does not necessarily reflect any position of the Offshore Technology Conference, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Offshore Technology Conference is prohibited.
Minimization of disparities between welltest and log-derived average permeabilities has always been an issue, particularly in carbonates where complex pore structures add on challenges to permeability estimation from wireline log data. The disagreement between permeability averages from logs and well tests originates from the combined effects of measurement-scale of static porosity components for permeability models, dual flow system of fractures and matrix, tensorial nature of permeability and the averaging techniques used.
The proposed workflow exploits rock-physics templates to identify and to quantify secondary porosity. Rock-physics templates employ conventionally derived total porosity and shear modulus as inputs. Fracture and vug porosity identified by the proposed workflow through rock-physics agree with other qualitative and quantitative evidences of non-primary porosity obtained from NMR, Image logs and core data1. Matrix and connected-vug permeabilities are computed, calibrated and integrated via "Chen-Jacobi?? connectivity-driven model2 by using NMR and acoustic log data. Fracture permeability is estimated from "fracture aperture?? and fracture-porosity by using image log data and rock-physics algorithms. The final permeability profile is computed with a selective-replacement step. This step ensures that in co-presence of matrix, connected vugs and fracture permeabilities at a given discrete depth level, the greater one would dominate and replace the lesser one. The final step in efforts of lessening the disparity between averages of wireline-driven and well test/DST permeabilities for a given interval is the usage of proposed averaging technique for the integrated wireline-driven permeability profile.
The rock-physics templates used in this study combine Kuster and Toksoz3 "inclusions?? theory with the Dvorkin-Nur4 granular media model (1996). We have observed appreciable correlations between secondary porosity driven from shear velocities against the secondary porosity determined from NMR and Image logs and core data. These correlations further provide routes for newer permeability models that can be solely based on the rock-physics.
Comparisons of permeability averages computed from wireline-driven permeability profiles against DST or welltest permeability showed significant improvements toward parity via proposed methodology and averaging technique. The workflow presented in this study is to guide the reader through numerous steps of the proposed algorithm in detail.
Frommer’s travel guide describes Jakarta as a “steamy, raw, chaotic place that puts all five senses in overdrive” and “not for the faint-hearted.” Surrounded by the Indian and Pacific oceans and located on the northwest corner of the island of Java, Jakarta is a dizzying and sprawling metropolis. A city that used to serve as an integral trading port for the kingdom of Sunda and the de facto capital of the Dutch East Indies, Jakarta continues to be an economic, cultural, and political epicenter for southeast Asia. Although the population of Jakarta proper is estimated at 10.2 million people, its urban area has spread to encompass more than 23 million people, trailing only Tokyo as the largest urban area in the world. This ultradense population has led to complex socioeconomic problems.