bluuebunny/crossref_metadata_2025 huggingface dataset:
Implications of Depletion-Induced Poroelastic Effects and Rock Mechanical Heterogeneity in 1D Geomechanical Modeling for Hydraulic Fracturing
Authors: Reddy Rathnakar , Aldahmani Asma , Martin A. N. , Singh Maniesh , Jaiyeola Abiodun Isiaka , Al Shehhi Hessa , Dutta Ashim
Published: November 2020

Hydraulic fracturing is the industry-proven technology for efficient exploitation of tight reservoirs. This study evaluated the technology by drilling horizontal wells through acid fracturing the low permeability gas reservoirs of a lower cretaceous carbonate formation, located onshore Abu Dhabi. 1D Geomechanical modeling was critical in determining fracturing strategy. Containment of fracture height growth was the most critical aspect of the fracturing process, as the target units were stacked within heavily depleted reservoirs. The challenge was that the rock strength, poroelastic behavior and stress paths vary significantly through the various formations overlying/underlying to target reservoirs. In addition, the depositional nature of the target reservoirs meant inherent variations in rock mechanical properties. Hence, a continuous profile of in-situ stresses and other rock mechanical properties was essentially mandatory for addressing the challenges for optimal placement of the horizontal drain hole and assessing fracture height growth.

Existing rock mechanical measurements, dipole sonic and image logs, as well as in-situ stress measurements were reviewed for 400ft (TVD) interval above and below the target reservoirs. Significant data gathering was managed in the vertical pilot hole of the first well to fill data gaps. A custom-built workflow was performed for 1D Geomechanical modeling in the first well, integrating poroelastic modelling with failure models. The study integrated drilling-induced and production-induced Geomechanical aspects into the 1D stress profile, including depletion-induced poroelasticity, shear anisotropy, and rock mechanical heterogeneity. Wellbore failures observed in vertical pilot hole and the in-situ stress measurements from offset wells were used for validating 1D Geomechanical model.

This work resulted in a rigorous 1D-stress profile that contributed to initial fracture modelling, whilst de-risking the fracture height growth into the depleted reservoirs and optimizing the choice of drain hole location within sub-units of the target reservoirs. The fracture gradient and breakdown pressures derived from 1D stress profile were found to be in very close agreement with that measured from the minifrac conducted in vertical pilot hole of the first well.

This paper presents a purpose-driven workflow designed specifically for these circumstances. The merit of the workflow lies in its systematic and methodical approach, providing solutions to various Geomechanical problems relevant to the target reservoirs, as well as the depleted reservoirs above and below. The results are beneficial for analogous fields where hydraulic fracturing is required to improve recovery of low permeability reservoirs in mature fields
Conceptual Study of New Type of Demulsifier for Low Cost Crude Oil Dehydration
Authors: Arayachukiat Sunatda , Piromchart Taradon
Published: November 2020

With conventional technique for crude oil dehydration, demulsifying agent or "Demulsifier" is used to separate oil, water and emulsion layers at a certain temperature condition. By this method, it leads to demulsifier loss in a crude oil product. The new concept of the dehydration is to recycle and reuse the demulsifer with minimal investment. With this concept, carbonate-based ionic liquids (CBILs) will be used as a demulsifier to separate emulsions and water in oil, resulting in a decrease in OPEX from demulsifier injection. In addition, CBILs represent versatile, green and easy-to-prepare chemicals. Laboratory test results show that the emulsion between oil and water is able to break within 15 minutes at 60°C which is comparable to conventional demulsifier. Moreover, the cost of CBILs is lower than conventional demulsifier. Therefore, this chemical is proved to be more attractive.
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

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 (<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

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

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

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

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

Paleozoic 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

Introduction

The 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.

Methodology

We 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.

Results

The 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.

Novelty

Although 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

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

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

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

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.
Drilling in the Digital Age: Harnessing Intelligent Automation to Deliver Superior Well Construction Performance in Major Middle Eastern Gas Field
Authors: Goodkey Brennan , Hernandez Gerardo , Nunez Andres , Corona Mauricio , Atriby Kamal , Rayes Mohammed , Carvalho Rafael , Herrera Carlos
Published: November 2020

Over the past decade, breakthroughs in digital technology have rewarded a variety of industries with a step change in productivity and efficiency. Despite this, the drilling industry has yet to benefit on a large scale from these advances and a significant amount of value remains untapped. This paper details the effort of a service company to leverage modern technology by introducing a drilling automation system in pursuit of achieving a higher degree of consistency and efficiency.

The drilling automation system described in this case study was deployed in the Middle East on two onshore gas drilling rigs in 2019. The deployment was an opportunity to validate the potential of modern drilling automation technology and prove its ability to consistently deliver value. Since the company had extensive experience in the region, the Middle East was selected as the preferred location for field trials. This ensured that the value of automation could be precisely quantified as performance benchmarks were well documented and available for comparison. The automation strategy relied on an intelligent decision management system capable of dealing with constantly changing drilling conditions in order to implement efficient, consistent, and standardized well construction operations, while enhancing safety and reducing NPT. When given authority, the system would take control of the rig surface equipment to enable full automation of most drilling actions and engage optimization engines to monitor and adjust parameters to maximize performance. The system was leveraged to eliminate the variability innate to humans and deliver consistent results, while consolidating the improvements, performance gains, and lessons learned that otherwise would tend to disappear or erode over time or through personnel replacement.

Throughout this document, insight is provided into the technology itself, the deployment process, implementation challenges, the agile development model, and the results achieved. In addition, as the introduction of automation is a major departure from the traditional human operated drilling process, an emphasis will be placed on the results of the change management strategies utilized.
Optimizing and Digitizing Asset Reliability Strategies to Minimize Cost and Maximize Performance
Authors: Reeve Stephen
Published: November 2020

The principles and tools of Reliability Centered Maintenance (RCM) have been around for decades, and most organizations understand the key elements that underpin a sound Reliability program. They invest time and money into creating and implementing Asset Management and Reliability improvement initiatives, in the effort to avoid unexpected failures, outages, safety exposures and costs to meet business performance goals.

Why then, time after time, do we see organizations struggle to deliver performance improvements and realize value from their efforts?

Are you experiencing any of these problems? High maintenance costs and unplanned downtimeUncertainty in the quality of strategies across the asset baseNo line of sight from the tasks being executed in the field to reliability strategy decisionsCreating and deploying optimal maintenance strategies is an excessively time-consuming processChallenges in benchmark asset performance and measuring continuous improvementsInconsistent maintenance plans and Master Data
Implementing a Production Performance Management Platform for Better Production Data Management, Improved Surveillance and Forecasting for Mature Fields – A Case Study in Sharjah, UAE
Authors: Moustafa Mahmoud , Guillen Armando , Jany Abdulla , Jain Siddharth , Kumar Hemant , Kedia Rachit
Published: November 2020

In production engineering, managing a mature field, where cost and job complexity increases as the reservoirs further deplete, analyzing a vast amount of data from different sources is required to understand the well and reservoir performance on a timely basis. Analyzing the full picture of the production system behavior and planning timely interventions is the key in managing these fields. As the E&P organizations go through the production lifecycle, it is increasingly difficult to keep this data in a structured format and a place where it can be easily accessed by the end users for their day-to-day workflows. More and more production data end up in Excel spreadsheets and other repositories creating what is known as data entropy. Legacy corporate data archives become a bottleneck in efficient production data management as they cannot scale efficiently.

On the data analytics side, production and reservoir engineers often rely on Excel spreadsheets or inefficient home-grown systems which restrict their ability to derive more insights from the data. Then there is the collaboration aspect. Emails and shared-drives become the de-facto form of exchanging analysis and data, but these resources are time consuming and error-prone due to misplacement, duplication and different vintage of several data files.

It is contradictory that the E&P industry despite its steady advances with digitalization and adoption of the latest technologies such as Artificial Intelligence and Machine Learning (to mention a few) continues to struggle with these fundamental data management issues. The implementation of a robust production data management system and basic analysis workflows are key to enabling effective digital tools and must be a top priority for any oil and gas organization consolidating their digital journey.

This paper talks about the success case of implementing a production data management system for the mature assets of a National Oil Corporation, encompassing 37 years of production data from various scattered archives. The implementation of the system was customized to suit the requirement of the end-users to enable maximum utilization and faster adoption of the technology. The conceptualization, implementation, customization and development of this digital transformation initiative along with lessons learnt and future development possibilities is demonstrated in this case study.
A Proposed Operational Method for Safe and Quick Retrieval of Deepwater Drilling Riser During Extreme Hurricane
Authors: Abimbola Fatai Akorede , Yang Jin , Uche Onuoha Mac Darlington , Liu Shujie
Published: November 2020

An operational technique designed for safe and quick retrieval of deepwater drilling risers during an imminent threat from hurricane or typhoon is developed by proposing a new drilling riser model with disjointed sections for easier and faster disconnection as against the conventional practice of retrieving the whole length of the riser which is tedious, time-consuming and has a huge risk of untimely evacuation of personnel and facilities. A theoretical method is used to model the drilling riser in freestanding disconnected mode and the resulting differential equation is solved numerically. The mechanical behavior of the drilling riser is analyzed for its survivability in freestanding disconnected mode in order to ascertain its useability when the impending typhoon is over. Parametric studies are carried out on key influencing parameters such as buoyancy can upthrust ratio, buoyancy can (BC) position, and riser thickness to arrive at an optimum condition that will ensure the survivability of the drilling riser when disconnected in freestanding mode. Fifteen parametric case studies are presented. In the first five cases, with a varying buoyancy can upthrust ratio but a fixed BC position and riser thickness, it resulted to obtaining a suitable buoyancy can upthrust ratio which is used in subsequent cases. The next six case studies gave a suitable BC position, and finally the last four cases for riser thickness. Results obtained from the fifteen cases are used to get the optimum condition for survivability of the riser in freestanding disconnected mode. A deepwater drilling riser designed in conformity to the optimum configurational conditions as presented in the analysis can survive in the face of an emergency situation such as hurricane or typhoon.
Clean Fuel, Smart Enterprise & Economic — Blending Economic
Authors: Zhang Wei
Published: November 2020

With the increased awareness of pollutant emissions' effects on the environment, minimizing greenhouse gases and particulate matter from cleanup activities, whether they're fossil or alternative fuel based, is at the core of green remediation strategies.

Refiners today are increasingly focusing on environment protection by implementation of clean fuel oil campaigns. The product specifications are tighter, and the refinery margins decrease significantly with existing refinery setups. Hence, refiners must revamp units and expand their product portfolio; as well as, asses: Digitalization: converting the refinery to a "Smart Enterprise" to achieve operational excellence and utilization of existing plant setup to archive the regulation and market changes via smart discovery.Optimizing capital investments to efficiently meet future market demand.

Digital Blending NLP/MINLP blending application can lead to operation results with accurate property estimation and profit increase in the nonlinearization of blending model. The MILP blending model is developed to improve blending accuracy by reducing quality giveaway and increase profit by optimizing blend recipe. Combined with LP technology for planning and scheduling as well as automated reconciliation, this creates Digital Planning and Scheduling and enables informed decision making. Refinery mission is to timely supply the highest value products at a competitive cost through a competent and optimized work force, while maintaining world class safety, reliability and environmental protection. We are in the advanced stages of implementing a digitalization program that gradually incorporates process control, digital twin, planning, scheduling and blending. This paper focuses on refined product blending optimization.

Daily blending execution to reduce quality giveaway by considering blends in future long period with tight specification. Spot blending: evaluating opportunities to improve margins Evaluating new products based on existing setup. The results support clean fuel production initiatives.
Troubleshooting Gas Compression System Using Data Analysis
Authors: Al-Aiderous Abdullah
Published: November 2020

The objective of this paper is to showcase the successful and innovative troubleshooting data analysis techniques in one of the gas compression systems in upstream gas oil separation plants (GOSP-A). The gas compression system using gas compressors, dry gas seal systems and due point controls is used in almost all of upstream operation.

These proven data analysis techniques were used to tackle major and chronic issues associated with gas compression system operation that lead to excessive flaring, mechanical seal failures, and off-specification products. Dry Gas mechanical seals are an important key element in gas compression and its lifetime represents a concern to the operation personnel. Most gas compression systems have a mechanical seal lifetime of 2 years which in some cases limit production, increase the potential of unnecessary flaring and increase OPEX significantly.

In this paper, comprehensive data analysis of the potential root causes that aggravate undesired premature mechanical seal failures and off-specification gas products will be discussed along with solutions to minimize expected impact. For example, improper gas conditioning that feeds dry gas mechanical seals increase the risk of premature failures. Additionally, improper product specification in some applications have been found to promote seal failures and incur additional flaring which is both costly and environmentally undesirable. In addition, after extensive analysis improper operation practices during compressor startups, steady state operation and gas conditioning have been linked with premature compressor failures, product off spec and safety device failures.

The field trial proved the effectiveness of the proposed innovative troubleshooting data analysis techniques in reinstating the gas compression unit in GOSP-A to its recommended design conditions, eliminated compressors and mechanical seal failures and avoided the off-specification products at the lowest operating cost. This innovative technique was based on deep and extensive process data analysis, evaluating operating and design data, reviewing international standards, benchmarking against other facilities, process simulation using Hysys, and finally the actual field trial.
Fluid Properties on the Main Path of Establishing Accurate Flowrates Measurement with Multiphase Flowmeters
Authors: Pinguet Dr Bruno , Kuczynski Konrad
Published: November 2020

Any flow metering devices provide the information at the meter conditions (i.e. line pressure and temperature). Still, the value and the volumes of water, oil, and gas need to be estimated at standard conditions. To convert them from the meter conditions to standard conditions, this requires additional information. In other words, measuring the flowrates requires knowledge of fluid properties (PVT) and phase transfer following pressure and temperature, and this is irrespective of the type of meter or technology used.

Multiphase and Wet Gas Flowmeters (MFMs) are particularly affected by the use of the PVT information for two main reasons. First, they can be working at very high pressure and temperature, which requires a significant correction to convert the measured flowrates to standard conditions. Second, because no separation is made, a large amount of gas can be dissolved inside the oil, or some of the condensates could be in gas phases at meter conditions when liquid at standard conditions.

A lot of published documents are showing the flowmeter performances under well-controlled conditions, but little publications are available about the impact of the fluid properties. The paper describes, first, the solution to adopt to evaluate the overall meter performance which is generic to all MFMs; second, explain that this cannot be addressed by any reference facilities where MFMs are tested due to the versatility of the produced fluids, third that using such miscible fluids lead to evaluate the MFM performance improperly at line conditions and worst leading to weak conclusions

The information necessary for the statement of the overall uncertainty performance is derived from a model; it is a generic set of information based on fluids properties, shrinkage and dissolution factors⋯ This paper will show that using a combination of this information, the flowrates at line conditions can be converted to standard conditions for any fluid and flow conditions. The use of these data with the intrinsic performance of the MFM provides the overall uncertainty expected by the oil and gas operators to estimate the performance of the well or reservoir.

If the oil and gas companies more accept the MFMs and the level of performance of the latest flowmeters are improving drastically. The PVT or fluid behaviour package cannot anymore be "hidden". The benefit is, with the use of the PVT and MFM performance, to provide the global uncertainty statement with a proper quantification of these cumulative uncertainties with the possibility for the end-user to review the performance of each MFM and how to keep them in the best working conditions in the field operations
Real-Time Measurement of Drilling Fluid Rheology and Density using Acoustics
Authors: Ofoche Paul , Noynaert Samuel
Published: November 2020

This paper presents a novel approach of continuously measuring drilling fluid rheology and density by use of sound signals. A unique apparatus is built with a series of pipe sections designed to exact pre-calculated dimensions to achieve equivalent standard shear rates as stipulated in the American Petroleum Institute (API) Recommended Practice 13D for measuring the rheology of oil-well drilling fluids (from 3 to 600 RPM). Acoustics waves are passed through the fluids of interest and their interaction is recorded and analyzed to deduce the density and rheological properties of the fluids.

The concept of resonance as demonstrated by the Barton's pendulums are the basis of the methodology. Sound signals are known to exhibit a damping effect when passing through various media. Pairs of sensors are employed in this set-up and their signal response are first characterized and calibrated with fluids of known properties. Electric current is converted into acoustic signals by piezoelectric sensors mounted of the flowline which are then emitted through the fluids desired to be measured without interrupting the flow. A matching sensor receives these damped signed and reconverts them back to electromotive potentials for recording by a data acquisition unit. The signals are then analyzed by applying statistical techniques to interpret and obtain the fluids physical properties.

Owing to the nature of the task, the goal of accurately achieving simultaneous measurement of density and viscosity is attained by applying an ensemble machine learning algorithm, known as Multivariate Random Forest. Pure chemicals and fluids of known properties form the training group on which the predictive model is built for subsequent testing on new mud samples flowing through each section. The pipe sections generate shear rates covering the standard range adopted in oilfield reports. Results from each pair of sensors are analyzed and compared with dial readings from rotational viscometers; these have shown to be within a narrow band of error.

As a result of this work, the voltage outputs are sent continuously and in real-time to a processing computer that converts the values to dial readings at standard shear rates, while not disrupting the flow. This can aid in the better monitoring and surveillance of the entire fluid system of the well, which is highly beneficial to well control. The system can also be arranged to acquire gel strengths or how the fluid behaves after a fixed period of rest. Improvements can be made on the current procedures for fluid characterization which have remained relatively static for many years. This work engages the disciplines of rheology, acoustics and machine learning, creating a mechanism for continuous and real-time drilling fluid surveillance critical to the enhancement of safe development of petroleum resources.
Visual Inertial System for ROV Positioning
Authors: Marques Filipe , Castro Filipa , Parente Manuel , Costa Pedro
Published: November 2020

ROV localization is essential for subsea operations and is commonly achieved by using acoustic sensors such as ultra-short baseline (USBL) and long-baseline (LBL). These systems are costly and prone to errors in specific scenarios, for example: operations in shallow waters or in proximity to subsea structures. In these scenarios, the operator may experience shadow areas where the accuracy will be compromised or position estimation is not possible at all.

Since a significant number of subsea inspection operations are performed under these circumstances, we propose a solution to estimate the movement of the ROV from its live video feed, providing a real-time estimation for ROV position in any scenario. The neural network returns motion estimation and orientation, acting as an inertial navigation system that is able to combine with correct, or replace the acoustic sensors position estimation.

The solution proposed in this paper was tested in a real subsea operation where acoustic sensors were not accurate. We describe the use case and how, with our solution and an in-house simulator, we were able to monitor the operation and create a replay of the entire mission in a simulated environment. We show that our solution is effective at estimating the ROV trajectory during the entire operation. Our method can be used to improve the accuracy of acoustic sensors and to replace them in situations where they are not able to operate, and, later on, we can see the entire operation in our digital twin.