bluuebunny/crossref_metadata_2025_split huggingface dataset:
Giving a Boost to Low Pressure Gas Well by Installing Gas Ejector
Authors: Ping Wei , Brian Macdonald 
Published: October 2019

Abstract
To further reduce the back pressure on low-pressure gas wells and increase reserves in a mature gas field, a gas ejector project was evaluated and proposed in 2014. Following on-site tests on several gas wells during 2015 the gas ejector was successfully put into service in 2016. Reservoir simulation estimates that 6.4bcf of incremental reserves will be achieved through gas ejector installation by decreasing inlet pressure to 50 psi from 100 psi. Very low capital costs and insignificant operating costs demonstrate positive economics. Existing facilities and availability of high-pressure gas source contribute to the success of this project. The gas ejector was designed and manufactured in China at far more competitive costs that could be obtained in the international market. The application of gas ejector during the past 2 years to reduce back pressure has helped to improve the economics in a mature gas field. Figure 1 shows the location of YC13-1 field facilities.
Conforming Production Logging Tool with High-Resolution Electrical Imager in Carbonate Reservoir
Authors: Al-Khalifa Nasser , Nugroho Cahyo , Memiche Lynda , Al-Blooshi Asmaa , Tiwary Asheshwar , Al-Moraikhi Haifa , Joshi Deepak 
Published: October 2019

Abstract
This paper presents results from integration of Production Logging Tool (PLT) data with image facies interpretation from high-resolution electrical images in a horizontal producer well in Kuwait. The Minagish Oolite Formation is a carbonate reservoir in the Umm-Gudair field which is characterised by packstone to grainstone with oolitic rich facies. Integration of fluid contribution along the producer hole from PLT measurement with image facies from electrical image logs will support completion strategy optimization.
A PLT was deployed in horizontal producer well of 6.125 inch (bit size) with coil tubing, when both flowing as well as shut-in conditions with choke size (128+128)/64″. Liquid rate from PLT 2981 Barel fluid per day (BPD) was in agreement with surface Portable Gas Oil Ratio (PGOR) test rate (2975 BPD). During drilling, high-resolution of electrical image was acquired. The electrical image has range of conductive (darker) and resistive (brighter) features, where conductive image shows porosity such as vugs and resistive image shows less pore space such as cemented patches in carbonates.
Based on PLT measurement, entire producer section was divided into six intervals, as below Interval A has 800 feet and interval E has 150 feet with production rate 56 BPD.Interval B has 300 feet which contributes 80 BPD.Interval C contributes 100 BPD and has 75 feet lateral section.Interval D has 300 feet lateral and contributes 92 BPD.Interval F contributes maximum liquid of 2590 BPD from 1350 feet interval.
On integrating PLT and facies analysis result, it was found interval F which gave highest liquid rate of 2590 BPD has higher statistics of conductive features. Second highest production rate coming from interval C is also showing high amount of conductive image. Low production rate interval E shows lower statistics of conductive features. Image analysis shows interval C and interval F having resistive patches, dominant moldic porosity and nodular limestone. Low production rate intervals are characterized by facies of clean massive limestone with less secondary porosity.
Facies analysis was exercised using electrical image log to characterize the reservoir. Identified image facies were mottled, bioclastic, vuggy, nodular, and clean to argillaceous limestone. Facies analysis based on conductive and resistive features from high-resolution electrical image has relationship with production rate from PLT. The study concludes that electrical image can be used to optimize completion design and hence to optimize production rate.
A Real-Time Indicator for the Evaluation of Hole Cleaning Efficiency
Authors: Alawami Mohammed , Bassam Mohammed , Gharbi Salem , Al Rubaii Mohammed 
Published: October 2019

Abstract
Drilling operations are considered a major cost in the development phase of oil and gas wells, which places huge emphasis on drilling efficiency as a leading factor in cost reduction and optimization. Nonproductive time (NPT) incidents such as stuck pipes must be minimized by careful planning and close well monitoring to enhance drilling efficiency. One frequent and global cause of stuck pipes is the inefficient removal of formation cuttings from the wellbore while drilling, which is the focus of the developed real-time model. Poor hole cleaning is also a major contributor to other NPTs such as loss of circulation and formation fracturing, which can be induced due to the high equivalent circulating density (ECD) caused by the presence of excess cuttings. Insufficient hole cleaning, if not tackled properly and in a timely manner, can lead to NPT incidents and consequently increases the drilling cost significantly. In addition to NPT reduction, proper hole cleaning can increase the rate of penetration (ROP) and reduce torque and drag. A tool that provides a real-time indication of the hole cleaning efficiency is therefore very valuable to have better control of the hole conditions, especially in critical wells. Such indicators will provide continuous monitoring of the wellbore and will allow immediate intervention for detected abnormalities.
There are several indexes that evaluate hole cleaning efficiency while drilling. The index that will be the core of the developed model is the Carrying Capacity Index (CCI), which is defined as the ability of a mud system to circulate the cuttings to the surface. The index is influenced mainly by the drilling fluid properties and flow hydraulics, which are both controllable factors that allow the rig crew to adjust on location to ensure sufficient cleaning of the cuttings.
The developed system automatically calculates the CCI in real-time. Thousands of raw values are generated from rig sensors continuously, which makes the calculations nearly impossible for a human to perform. The model is developed to take in the raw data and use it as an input in conjunction with some well details, such as hole size and casing size, to generate the CCI. This system takes us one step closer toward the ultimate goal of having an integrated and fully automated hole cleaning evaluation and intervention tool that does not require any human involvement.
A Straight Line p/z Plot in Multilayer Tight Gas Reservoirs
Authors: Jongkittinarukorn Kittiphongs , Last Nick 
Published: October 2019

Abstract
Application of conventional material balance methods to evaluate multilayer tight gas reservoirs gives misleading results. This is due to the phenomenon differential depletion, as a result of which very long shut-in times are required to get the correct average reservoir pressure, and pressures from static BHP surveys are generally significantly lower than the average reservoir pressure. This study proposes a new approach which yields a straight line on the p/z plot, without a long shut-in period.
This paper proposes to use data from production logging which can be used to perform selective inflow performance (SIP) analysis. From SIP the pressure and productivity of each reservoir can be estimated independently. The average reservoir pressure of the multilayer system can then be calculated from each layer's reservoir pressure weighted by its hydrocarbon pore volume. The method therefore requires estimation of the hydrocarbon pore volume ratio (Fv). A guideline for Fv estimation and the effects of Fv on the p/z plot will be discussed in this study. With this new average reservoir pressure, material balance methods will give a straight line, similar to one for a single layer gas reservoir.
Without loss of generality, this study focuses on a 2-layer gas reservoir: a high-permeability layer (layer 1) and a low-permeability layer (layer 2). The results can be applied to multilayer gas reservoirs. Fv is defined here as the ratio of the hydrocarbon pore volume (HCPV) of layer 1 to the HCPV of layer 2. For a given Fv, together with individual layer pressures from SIP analysis, the average reservoir pressure can be calculated. Different values of Fv yield different profiles on the p/z plot. A straight line can be obtained only from the correct value of Fv. If the value of Fv is incorrect, the calculated pressure will be deviating from the theoretical straight line. High and low values of Fv lead to underestimation and overestimation of GIIP and reserves, respectively. During the early production period, most of the production is from layer 1 which dominates the early-time response. The differential pressure between layer 2 and layer 1 (p2 – p1) is increasing during this period. After layer 1 is partially depleted, production from layer 2 starts to increase significantly, and dominates the late-time response. The value of (p2 – p1) reaches a maximum and then decreasing. Beyond this point, all profiles for different values of Fv lead to the correct GIIP and reserve. Then the uncertainty will be reduced dramatically.
This study should be beneficial to engineers who wish to estimate GIIP and reserves. It is theoretically sound for multilayer gas reservoirs, both tight and high permeability reservoirs, for both with and without cross-flow. With this new approach, engineers can apply material balance methods to better understand multilayer gas reservoirs and to make better reservoir management decisions.
Breaking the Challenge of Brownfield by Delivering 10,000 BOPD with Implementation of Gas Lift Supply Strategy. The Success Story of Facility Upgrading, Integration and Optimization in Oil Field
Authors: Muhammad Kahfie Rizky , Kurniawan Farid , Saputra Reza 
Published: October 2019

Abstract
Sanga Sanga block has been producing for more than 50 years, with most of the oil production from depleted reservoirs with 60% of base production decline. Extending the wells’ lives requires aggressive artificial lift development both in subsurface and surface. The implementation of gas lift supply strategy is very critical to fulfil gas lift capacity requirement also with sufficient injection pressure. This paper will describe gas lift supply strategy consisting of facility upgrading, along with integration and optimization of the existing facility.
Thorough project planning and procedures have been established to ensure the optimum oil development. The gas lift facility upgrade consists of three main projects as follows. The first was a major project of an additional high pressure compressor for gas lift. One unit in the Mutiara field and two rental units in the Nilam field have been installed to increase gas lift capacity.
The second project was the installation of an interconnected gas lift network to ensure availability and coverage of gas lift system along from gas lift source to the furthest well location. The last project was the optimization of the existing production facility, injection facility and trunkline to reduce capital cost.
This project was completed as scheduled and successfully delivered 10,000 BOPD in the last four years. It was supported by an additional 27 MMscfd supply from the HP Compressor project as a gas lift source.
A length of more than 35 km of gas lift system was completed in 6 producing fields to eliminate gas lift blank spot. Gas lift injection pressure has increased from 300 - 1000 psig to 1400 to – 1800 psig, enabling more drawdown in the gas lift wells. The interconnection system installed gives two benefits: flexibility of gas lift source during compressor maintenance and also the distribution of High Pressure and Medium Pressure gas. Sanga Sanga also managed to reduce the unit price of the individual gas lift injection facility by 30% through a bulk procurement strategy, utilization of idle equipment and the modification of existing equipment.
Leveraging Oil and Gas Data Lakes to Enable Data Science Factories
Authors: Antonio Daniel , Sadlier Andreas , Winston Joseph 
Published: October 2019

Abstract
As oil and gas companies undergo their digital transformations, they typically first focus their efforts on analytic and machine learning solutions. They expect immediate advancements in automated operations and detecting predictive failures. The solutions generally rely on small-scale proof of concept exercises to demonstrate their worth. Far too often, this approach relies on manually collected datasets. Ironically, these consume the majority of the project's time and resources; consequently, they fall short of their promise to yield significant financial returns.
Organizations must centralize available information into a corporate data lake to enable data scientists to access all available data. New challenges also arise from information governance and data management because this data originates from different business units with their own goals and concerns. Rather than focusing on the analogy of a data lake as a storage methodology for information, it is useful to view a data lake model as a manufacturing facility that produces analytical insights and enhanced capabilities.
Just as a manufacturing facility is organized around specific processes to deliver finished goods, a data lake should provide all capabilities necessary to transform raw data into valuable assets for oil and gas organizations. The data lake must therefore feature several analogous capabilities and properties. These include a receiving dock, quality assurance/quality control (QA/QC) stations, warehousing, and tooling and engineering, as well as flexible, lean assembly lines to build new products and shipping capacity to deliver the finished goods to customers. By applying successful manufacturing techniques to the data lake design, oil and gas companies can efficiently develop and maintain assembly lines for manufacturing analytical insights.
This paper explains the similarities between delivering analytics and manufacturing processes. It also describes the data lake functionality. Each part of the process provides a critical component for generating analytical results and can be managed like its manufacturing counterpart to deliver lean processes that enable more efficient data science results.
Estimating PVT Properties of Crude Oil Systems Based on a Boosted Decision Tree Regression Modelling Scheme with K-Means Clustering
Authors: Almashan Meshal , Narusue Yoshiaki , Morikawa Hiroyuki 
Published: October 2019

Abstract
Machine learning has been successfully implemented for the past 20 years in estimating reservoir fluid properties competing with the empirical correlations. One of the most commonly utilized modelling schemes is the artificial neural network which is known for its black-box problem that does not show the steps taken to reach the final estimation of the fluid properties. This study offers a different modeling approach that overcomes the limitations of the current implemented modeling scheme providing users with better predications and deeper understanding of the key input parameters in modeling. The proposed model predicts the bubble point pressure (Pb) and the oil formation volume factor at bubble point pressure (Bob) as a function of oil and gas specific gravity, solution gas-oil ratio, and reservoir temperature by a boosted decision tree regression (BDTR) predictive modeling scheme. The K-means clustering algorithm is performed as a preprocessing step based on the Pressure-Volume-Temperature (PVT) input features to increase the prediction accuracy. In addition, the predictive power of the built K-means clustered BDTR model implemented in this study is compared against the most commonly used empirical correlations, the ANNs, and the standalone BDTR model. Moreover, the feature importance of predicting Pb and Bob is discussed. The universal dataset used in building the predictive model consists of 5200 experimentally derived data points representing worldwide crude oils covering a wide range of geographical regions. The built BDTR model is more accurate and it outperforms the most commonly used empirical correlations and the previous machine learning models in predicting Pb and Bob in terms of the average absolute percent relative error. Furthermore, the proposed model can be integrated into simulators and it can also be applied towards predicating other oil and gas properties, in the gas-liquid two-phase flow pattern identification, and in predicting rock properties. As an interpretable approach in predicting the PVT properties of crude oils, the proposed model can be used as an alternative modeling scheme in PVT characterization where the importance of the input features can heuristically and accurately be determined. This can be applied towards preventive maintenance and anomaly detection studies where prediction decisions can be further investigated by the interpretable representation of the decision trees. In addition, it is the most accurate model to date in predicting the bubble point pressure and the oil formation volume factor at bubble point pressure of crude oils.
Cost Effective Long Interval Wellbore Cleanout Using Wireline Technology: Field Application in Peciko Field, East Kalimantan
Authors: Rahman Risal , Siamsyah Kurniawati Pratika , Nadrul Jamal Muhammad 
Published: October 2019

Abstract
Mahakam field was experiencing gas production decline in some offshore wells. Previous interventions done in the past indicated the high probability of scale and sand which had reduced the production flow path, it had also prevented perforation gun from reaching the target depth in order to perform additional perforation to increase well potential.
Considering existing perforated reservoirs in the wells were still productive, Pertamina Hulu Mahakam (PHM) preferred to use well clean out method without invading/damaging open reservoirs. The additional cost of lost or deferred production was always unwanted. The deployment of traditional rig or coiled tubing unit to help restoring production came with considerable cost due to the equipment and personnel requirements. Consequently, PHM continually looked for new techniques to reduce this challenge. Milling on wireline, where applicable, was one solution since it could be performed in rig-less environment.
The delivery of wireline deployed milling services had been provided by conveying a specially designed milling tool which had its own built in control, power and drive section. The bit design could be customized to make the tool fit for specific application.
For a milling tool to operate properly on wireline, there were 2 physical principles that were needed to be dealt with; weight on bit (WOB) and anti-torque. To address this, wireline tractor was used.
This system fit perfectly with the context of mature field; simple, cost-effective, safe and easy to handle.
Application of Fuzzy Logic for Improvement of Perforation Gain Predictability in Multi-Layered Giant Mature Gas Field
Authors: Rau Irfan Taufik 
Published: October 2019

Abstract
A giant gas field that covers 75 km in length and 15 km in width has been producing since 1990 from approximately 1,200 wells which are located in 34 platforms. Deposited within a deltaic environment with enormous multi-layer sand-shale series, the wells undergo commingled production with an average of more than 30 reservoirs per well. With a total of approximately 700 perforation jobs included in more than 4000 well intervention jobs per year, the field is considered as the most complex field in the PSC Block in terms of operations. Prioritization of these perforation jobs are based on the perforation gain of each job. Therefore, properly estimating the perforation gain is crucial in order to efficiently and effectively manage and prioritize well intervention jobs.
Hypothetic approaches, for instance productivity index driven from Darcy's equation, may not be straight-forward due to incomplete and imprecise data measurement. Overwhelming operations workload in the field limits the number of data acquisition jobs performed. Consequently, required data to estimate perforation gain such as skin, pressure and drainage radius becomes limited. An alternative approach using artificial intelligence called fuzzy logic was introduced. Being a soft-computing pattern recognition method that allows imprecise input to yield output, fuzzy logic fits well with the nature of high uncertainty in geosciences data. The one-year study is conducted on reservoir basis using well monitoring results to split well level gas rate into individual reservoir gas rate. In order to ensure that proper data are incorporated in the model training, processes of data filtering must be undertaken. Therefore, implementing fuzzy logic to estimate perforation gain includes 3 main steps: (1) Preparing and Filtering Training Data Set; (2) Building the Fuzzy Model; and (3) Performing Blind Test.
After series of trial and error process, the model has reached its minimum error without compromising sense of engineering and generality. The fuzzy model results in 960 fuzzy rules and 5 input parameters: netpay, porosity, drawdown, mobility and water risk. Afterwards, the blind test shows that the resulting output from fuzzy logic correlates well with the realized gas rate both on reservoir level and well level, with maximum R-squared value of 0.7. The study is limited within the scope of current best practice for unperforated reservoirs and further study would be required to estimate the perforation gain from unconventional perforation methods and re-perforations. This method of estimating perforation gain using fuzzy logic has been implemented on daily basis with the aim to improve the efficiency and effectiveness of managing and prioritizing well intervention jobs in such a complex environment.
Source Mechanism and Stress Inversion for Hydraulic Fracturing Induced Microseismicity in Glutenite Reservoir
Authors: Feng Runhua , Memon Shoaib , Zeng Lingping , Xie Quan , Jia Suzie Qing , Zhang Hongliang , Du Daijun , Pu Wanfen , Sarout Joel , Sarmadivaleh Mohammad 
Published: October 2019

Abstract
The microseismicity associated with hydraulic fracturing in unconventional reservoir (i.e. shale gas play) has been investigated in the past several decades. Few experimental studies with respect to the focal mechanism and stress inversion was conducted, especially for Glutenite reservoir. In this study, the glutenite core was taken from the underground of 2600 m. Next, we performed scaled hydraulic fracturing tests on the cubic core (50×50×50mm) under geological principle stress condition in true tri-axial stress cell. Meanwhile, we monitored wellbore and pore pressure, and micro-seismic events during the fracture propagation from six faces of the cubic rock. Micro-seismic survey and events were interpreted to identify the induced fractures distribution in three dimension. Source mechanism and stress inversion were analyzed by moment tensor decomposition.
The correlation of failure plane from microseismicity and tested sample implied that the microseismic events were accurately localized. The distribution of microseismic events from secondary and reopening tests indicated that the hydraulic fracturing induced microseismicity are mainly caused by significant tip effect (i.e. reactivate preexisting natural fractures). Based on source mechanism analysis, we found that the most of the failure are dominated by double-couple (DC). The correlation between original principle stress state and the one from STESI inversion indicated that the direction of principle stresses, especially for σ2 and σ3 inversed from reopening test, can be highly influenced by the hydraulic induced fracture or weak planes during secondary fracturing test.