bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experiment and Simulation of Gas Channeling Control Technique During CO2 Flooding in Normal Pressure Tight Oil Reservoir of South Ordos Basin
Authors: Xu Ting , Wei Yi , Ma Tao 
Published: November 2020

Abstract
HH Chang 8 tight sandstone reservoir in the South Ordos basin has been developed for more than 7 years on depletion mode. Because of normal pressure (0.8±), extra-low permeability (&lt;0.3×10−3μm2), heterogeneous oil bearing, development effect is not satisfied. A pilot well group was selected to conduct CO2–foam flooding in 2015, while gas channeling phenomenon is so serious that the oil stimulation is poor. To enlarge CO2 sweep efficiency, it is necessary to evaluate adaptable plugging agents and simulate the inter-well fracture network blocking effect in tight reservoir with multiple-scale complicated fractures developed.
In this study, four different openings fractures were artificial fabricated in natural crop cores based on the fracture characteristics of the reservoir. Injection performances and plugging capability of the starch gel and the ethylenediamine were evaluated when the gas channeling phenomena appeared during CO2 flooding by the laboratory experiment and the gas channeling control adaptability were analyzed. By experiment numerical simulation, the fracture conductivity multiple index (MULTX) was determined to simulate the fracture blocking effect. Then the reservoir simulation was conducted, classified MULTX were further optimized based on plugging characteristics of complicated fractures network during CO2 flooding.
Experiment results indicate that the starch gel is suitable for blocking off fractures with openings between 0.42∼0.65mm. While the ethylenediamine is suitable for control fractures with openings less than 0.24mm, especially for 0.08mm. In Chang 8 tight reservoir, the medium- to large-scale fracture permeability is higher than 200 × 10−3 μm2, the starch gel was applied to control the serious gas channeling. For small- to medium-scale fracture, the ethylenediamine was applied to mitigate gas channeling. Fracture blocking experiments were numerically simulated to determine MULTX being in the range of 10−8∼10−6. Numerical simulation of well group blocking was conducted in the complicated fracture G&G model, MULTX was further optimized to be lower than 10−6 for the medium- to large-scale fracture and 10−4∼10−3 for the small- to medium-scale fracture, keeping seepage capacity of micro-cracks. Well group CO2 flooding shows a promising improving oil recovery, sweeping efficiency is increases from 1.4% to 24.1%, the utilization factor is 4.35 t/t oil, compared with 1.3% recovery factor of depletion mode, by 10 years CO2 periodic injection, recovery factor is enhanced by 3.76%.
It is a big challenge to effectively develop the normal pressure continental deposit tight oil reservoir. In China reserves of tight reservoir amount to a large proportion in the unconventional oil reserves. Study results provide technology support for CO2 flooding extended test in HH Chang 8 tight reservoir, also make CO2 flooding method successful application possible in other low pressure tight sandstone oil reservoirs.
Casing Shearing Failure in Shale Gas Wells Due to Fault Slippage Caused by Hydraulic Fracturing-Case Study
Authors: Liu Kui , Chen Zhaowei , Zeng Yijin , Cao Hu , Tao Qian , Wang Zhifa , Zhang Hongbao 
Published: November 2020

Abstract
According to the analysis of casing deformation events and the achieved micro-seismic data in four shale gas wells, the slip of the fault is treated as the main reason for casing deformation problems in shale gas wells in Changing. Analytical model for calculating fault slippage is established based on elastic mechanics and the slippage of faults around 4 shale gas wells is calculated. The calculated results show that both the height of fault reactivation area and the angle between the maximum in-situ stress and fault have significant effect on the fault slippage. The location of the intersection point where casing goes through the reactivation area of fault can significantly affect the casing deformation. The closer the intersection point is to the center of reactivation area, the larger fault slippage and the serious casing deformation occurs. Case study in well Ning201-H1 shows that the slippage of fault at total depth 3490 of this well is 30.5mm, and the corresponding casing deformation is 10.5mm which is in agreement with field data. The casing deformation in drilling platform H19 is much more serious due to the extremely complicated faults and the large reactivation area. The maximum casing deformation reaches 35mm in well H19-5. The results of this study have significant reference value for understanding the mechanism and controlling methods for casing deformation in shale gas wells during hydraulic fracturing treatment.
Rigless ESP Replacement Solution Achieved a High Production Rate Target Offshore, China with a Run Life More than 7 Years
Authors: Zhao Mengpan , Zhang Qiang , Hong Yuandong , Zeng Liam 
Published: November 2020

Abstract
Due to uncertainty of reservoir properties, one client in South China Sea was faced with a situation, which required flexibility to upgrade the ESP in case the actual production rate would be higher. Rigless ESP change-out will tremendously save costs and reduce production deferment.
However, the main concern of client is to ensure that the Rigless ESP system itself is reliable enough in an ultra-high production well and will not cause more workover jobs. The most common reasons of Rigless ESP System failures are due to vibrations, this will be more challenging as the production rate increases.
This paper introduces the design philosophy adopted by Rigless ESP System, which has proprietary hanger system and specific wet connector assembly to protect the whole system from vibration and increase robustness. Also details the case history of Rigless ESP System that was deployed successfully and achieved a record 7 years run-life. Field applications validate the reliability, durability and robustness of Rigless ESP System, this solution is an effective way to save rig cost and reduce production deferment during ESP workover operations.
A New Slimhole Neutron Porosity Logging While Drilling Tool Using a Pulsed Neutron Generator Source
Authors: Mao Weimin , Ai Weiping , Sheng Limin , Dou Xiurong , Wang Peng , Hong Difeng , Gao Wenkai 
Published: November 2020

Abstract
Neutron porosity measurement could provide basic and important parameters of the formation while drilling and it is indispensable for the real-time comprehensive formation evaluation and geosteering. As deeper and more complex reservoirs are being drilled, the needs of slim-diameter tools are increasing. This paper presents a new neutron porosity LWD tool using a generator source. The tool can operate in environments with pressures up to 172 MPa and temperatures to 175 degC, and can be combined with the 4.75-in geologic steering drilling system.Most neutron porosity tools, including wireline and LWD devices, use Americium-Beryllium (AmBe) and Cf-252 radiological sources which have great impact on the environment and have problems of handling, shipment and storage. This tool in hardware includes a pulsed neutron generator (PNG) and three Helium-3 (He-3) filled detectors. With two different source distance combinations, the tool could be working in multi-detection or high-sensitivity measurement mode which would adapt the tool to different kinds of formation. Standard well tests show that the data of this tool is in good agreement with the wireline logging tools. It also get good results in temperature and pressure test. All these preliminary tests and data analysis show that the tool could be very helpful in formation evaluation and reservoir monitoring in HT-HP conditions. This paper also presents compatibility design of the tool and possible applications as a part of the 4.75-in geosteering drilling system.
The Concept of Smart Hydrocarbons and Smart Reservoirs in the Development of Oil and Gas Fields in Karst-Fractured Carbonate Reservoirs
Authors: Petukhov Alexander 
Published: November 2020

Abstract
Our studies undertaken at many oil and gas fields in different basins show that fractures separate reservoir rocks into differently-sized blocks that are complex self-similar fractal structures whose behavior is described by Pareto's common universal law. Based on this law, a fractal model of fractured reservoir was developed. It includes several hierarchical levels of matrix blocks and fractures, sometimes ten and more. In the proposed model, not only the sizes of the blocks are in the ratio of 1.618, and permeability of the fractures changes in the ratio of 1.618, which allows to reproduce the daily and cumulative oil and gas well production according to power law distribution and Pareto's law. According to the laws the article deals with one of the development paths which we proposed to call "intensive" [15]. Currently, this path of development is almost ignord by oil and gas companies, which, in order to increase the capitalization of their assets, are aimed at using modern digital technologies, using the capabilities of artificial intelligence, big data, neural networks and machine learning, etc. However, we believe that the path can make a significant economic and environmental contribution to the development of hard-to-recover resources in tight fractured carbonate reservoirs. The proposed development path is based on an understanding of the "smart" nature phenomenology and training of modern creative professionals in the base oil and gas universities. This development path allows to substantially reduce expenses while obtaining higher daily and cumulative production of hydrocarbons and preserving the natural potential of fractured reservoirs created by the nature itself. Today's specialists working on development of information technologies call this development path "nature-like technologies". However, considering natural fractured oil and gas reservoirs, we can talk about a purely natural phenomenon.
Utilization of Geochemical Signatures for Unconventional Reservoir Characterization, Saudi Arabia
Authors: Hussain Maaruf , Amao Abduljamiu , Al-Ramadan Khalid , Babalola Lamidi , Humphrey John 
Published: November 2020

AbstractPaleozoic tight sandstone and shale reservoirs in the Kingdom of Saudi Arabia are considered potential sources of natural gas that will meet the growing domestic demand. In order to enhance their exploration and exploitation, research focusing on the quality of these potential gas reservoirs has tremendously increased in the past few decades. Most of the Paleozoic reservoirs have similar lithologies and are poorly constrained biostratigraphically, making facies analysis, stratigraphic correlation, and geosteering problematic. This paper describes how chemostratigraphic markers are used to identify and separate individual lithologies. In this study, 320 data points were acquired from continuous core samples obtained from three Paleozoic formations. Raw elemental spectral data were acquired using a high-resolution tabletop µXRF system. The spectral data were used as geochemical signatures to provide a better understanding of the geochemical composition of each sample. Multivariate statistical analysis including principal component analysis (PCA), pairwise correlation and hierarchical clustering of principal components analyses (HCPC) were used to characterize the acquired geochemical signatures.Eleven chemofacies within three lithofacies were identified based on PCA and HCPC analyses of the geochemical signatures with clearly defined boundaries. Stratigraphic distribution patterns of the geochemical signatures (e.g. Si, Ca, Mn, Al, K, and Fe), such as sharp truncation at the upper and lower bounds, clearly defined each lithofacies. Thus, key marker elements for each lithofacies and chemozones within it were identified. Formation intervals were clearly defined based on the chemozones, even where the lithologies appeared homogenous. The overall results indicate sediment deposition under varied depositional settings, from fluvial to shallow marine environments. The results demonstrate that elemental composition analysis can be used as a discriminating tool for resolving stratigraphic uncertainties due to its ability to document subtle variations among samples that appear homogenous with other techniques. The definition of key marker elements for each chemofacies will serve a useful tool to support geosteering operations for better well placement.
Machine-Learning Model for the Prediction of Lithology Porosity from Surface Drilling Parameters
Authors: Magana-Mora Arturo , Abughaban Mahmoud , Ali Ammar 
Published: November 2020

AbstractIntroductionThe accurate characterization of the lithology porosity is critical for geological interpretation and decision making in petroleum exploration. For this, wireline logging (including sonic, neutron porosity, and density, among other logs) is often used for the characterization of geophysical data performed as a function of wellbore depth. The common practice in the oil and gas industry is to perform the wireline logging for every new well, which is a lengthy and expensive operation. Therefore, the objective of this study is to use the historical logging data and surface drilling parameters to derive machine-learning (ML) models able to identify the different lithology classifications.MethodologyWe used historical logging data and surface drilling parameters to derive ML models to predict the following lithology classification: 1) porous gas, 2) porous wet, 3) tight sand, and 4) shaly sand. These models can predict these classifications without running wireline logs in the new wells. In this approach, the four lithology classifications are defined from the sonic, neutron porosity, gamma-ray, and density logs from historical data and are considered as the learning target/labels for the ML model. Therefore, the ML model learns the relationship between the surface drilling parameters and mud weight with their respective lithology classification. Finally, the model is capable of being executed in real-time, improving crew decision making.ResultsThe results obtained from a stratified 5-fold cross-validation technique demonstrated that the random forest model was able to learn from the data with an accurate classification for the four lithology porosity categories. The derived ML model obtained an average of 89.66% and 89.20% for precision and recall, respectively.NoveltyAlthough many studies have suggested the use of ML to imputing logging data, the inputs of these models are the data from other logs. Conversely, our proposed approach utilizes the wireline logging data only during the training of the model for assigning the porosity classification as labels. As such, the model learns the relationship between drilling parameters and the associated labels. This approach not only simplifies the learning of the ML but eliminates the need to run wireline logging in new wells, considerably reducing time and costs.
Production Optimization and Value Maximization of a Giant Ultra-Sour Gas Carbonate Reservoir, Onshore Abu Dhabi
Authors: Ghassan Majed , Fernandes Martin , Al Ali Onood , Kaczorowski Norman 
Published: November 2020

Abstract
This paper describes how the reservoir team at ADNOC Sour Gas developed the ability to dynamically adjust and manage their production strategy based on plant product output and market requirements, driving profitability and maximizing value of the sour gas assets of the UAE.
The reservoir team developed and successfully implemented an extensive data acquisition program, enabling adequate characterization of a giant ultra-sour gas carbonate reservoir in the Late Jurassic Arab Formation in the western area of Abu Dhabi.
The field is located in the southern part of UAE, in the Liwa province, and covers an area of 57 km2. It consists of four main reservoir zones: Arab A, Arab B, Arab C, and Arab D. Current development is focused on the central part of the field with most of the wells dedicated to Arab C. Future development plans will focus on the southern and northern areas of the field.
Early during the appraisal stage, the data suggested the existence of an areal gradient in composition across the reservoir. As such, a clear understanding of this areal distribution in addition to the usual reservoir gas composition, properties and behavior was essential in optimizing field production and maximizing value.
Over the course of field development, reservoir fluids from different well locations were sampled and analyzed. Various issues were encountered during this process including H2S stripping in down hole samples, contamination from stimulation fluids and quality assurance and quality control concerns in lab measurements. Resolving these issues allowed a coherent understanding of the compositional variation in the Arab Formations.
To properly model the compositional variation, an innovative methodology was implemented by the team to initialize the dynamic model. The methodology consisted of two major steps. Firstly, PVT data was analyzed and correlations between H2S and other components were developed. Secondly, through PETREL, compositional maps were created. Ultimately, each grid block was assigned a unique composition honoring the areal variation in composition across each reservoir zone.
In addition, empirical correlations between fluid components and plant product streams were developed through material balance analysis. Using product models, these correlations were input into the dynamic model which allowed estimated plant products to be output directly from simulation runs. Simulation forecasts of estimated plant products were later verified by actual plant yields, giving confidence in the methodology implemented. Further, this method allowed a quick turnaround in production planning and optimization thereby reducing the reliance on a fully-fledged plant simulator for short term gains and quick wins.
Experimental Study on the Hydraulic Fracture Behavior When Meeting Large Particle, Layer Interface and Natural Fractures
Authors: Wang Tao , Chen Mian , Wu Jun , Lu Junkai , Luo Cheng , Ju Yingtong 
Published: November 2020

Abstract
To understand the behavior of hydraulic fracture in the rocks with heterogeneous particles, layer interface and natural fractures, this study carried out a series of experiments of hydraulic fracturing, utilizing a transparent device and an optical microscope for observing. The whole process of rock deformation during the fracturing had been record automatically and the digital image correlation method was used to analyze the deformation and strain of rocks. Several hydraulic fracturing experiments were conducted on difference stress state and rock features. The results showed that large particles had much influence on the fracturing path which could over the stress restriction and make the fracture divert, bifurcate or stop. The interface between two layers with different particle size may make fractures deflect like light refraction. The strain analysis showed that before the final fractures generated represented by a pressure drop, much deformation along the final fracture path had already occurred. That meant that the fracture path had been determined before the pressure drop, and the fracturing fluid influence the fracture path mainly by the penetration. This study can help know the law of hydraulic fracturing in real rocks and optimize numerical fracturing models.
Facilitating the Identification of the Nannofossil Species in Cretaceous of Abu Dhabi Using Artificial Intelligence
Authors: Tamamura Hitoshi , Yamanaka Motoyoshi , Chiyonobu Shun , Yamada Goro , Hasegawa Sou , Totake Yukitsugu , Nanjo Takashi 
Published: November 2020

Abstract
Reliable chrono-stratigraphic correlation is crucial in the reservoir evaluation. Lithostratigraphic correlation is often inconsistent with depositional age due to high heterogeneity in lithology. Geological age determination based on nannofossils in Middle East gives a better control for the stratigraphic correlation, however it requires skilled experts and time for appraisal. This contribution explores if today's emerging AI technology provides a solution, by facilitating the identification of the nannofossil species on smear slide images. The result of our exercises shows that a deep-learning-based object detector reasonably and quickly identifies nannofossil species on micrographs and can deliver information useful for the geological age assessment. We find that separating detection and classification procedures improves the performance of the object detector. Further data collection and model tuning of the object detector are needed for the improvement of the object detection accuracy, especially for nannofossil species rarely observable.
Prediction and Optimization of Rate of Penetration using a Hybrid Artificial Intelligence Method based on an Improved Genetic Algorithm and Artificial Neural Network
Authors: Li Chengxi , Cheng Chris 
Published: November 2020

Abstract
Oil and gas exploration is facing an ever-increasing demand for cost-efficient drilling operations. Improvement of the rate of penetration (ROP) of the drill bit is key in solving the aforementioned challenge. The objective of this study is to develop a more accurate and effective predictive and optimization model for ROP that utilizes a hybrid artificial intelligence model based on an improved genetic algorithm (IGA) and artificial neural network (ANN) for further optimization of drilling processes.
Real field drilling datasets such as the bit type, bit drilling time, rotation per minute, weight on bit, torque, formation type, rock properties, hydraulics, and drilling mud properties are collected and input to train, validate and test the developed IGA-ANN model for ROP prediction and optimization. We apply a Savitzky-Golay (SG) smoothing filter to reduce the noise from the raw datasets. We apply IGA to find the optimal structures, parameters, and types of input of the ANN. By using supplementary population, multi-type crossover and mutation and adaptive dynamics probability adjustments, the developed model, IGA-ANN, avoid the limited optimization and local convergence problems in the classical Genetic Algorithm (GA). Using the developed prediction model, we obtain the optimal operational parameter within a region considering drilling equipment capability and wear to maximize ROP.
From numerical results, we find that the optimal structures and parameters of the ANN can be obtained efficiently by the developed method. For comparison, we compare IGA-ANN with the classical wrapper algorithm for parameter selection. The results indicate that IGA-ANN is more stable and accurate than the wrapper algorithm. We compare the true ROP and predicted ROP from the developed IGA-ANN model using accuracy indicators such as root mean square error, mean absolute error, and regression coefficient (R2). For comparison, the accuracy of the classical regression model is presented. We find that IGA-ANN yielded more accurate test results (R2 = 0.97). We compare the results of IGA-ANN trained using SG smoothing filter processed data and raw data. The test results show that the noise reduction approach used is very efficient in increasing the accuracy of IGA-ANN. Using the developed model, we optimize the choice of drilling operational parameters within a region considering drilling equipment capability and wear. We find that the optimization can increase average ROP significantly.
We develop an efficient and robust algorithm, IGA-ANN, for ROP prediction and optimization. Compared with the classical wrapper algorithm and multiple regression model, the IGA-ANN can efficiently optimize the structures, parameters and types of inputs of ANN to achieve higher ROP prediction accuracy. By utilizing the developed model, we can efficiently maximize ROP and minimize the drilling operation cost.