bluuebunny/crossref_metadata_2025_split huggingface dataset:
Technology Focus: Data Analytics (October 2023)
Authors: Yang Junjie 
Published: October 2023

Throughout the present decade, the oil and gas industry is experiencing and enduring digital transformation, marked by continuous evolution. In the pursuit of increased efficiency, companies have embraced cutting-edge technologies in artificial intelligence and big data, effectively automating their existing work flows.
Having undergone extensive exploration and refinement, the debate between physics-based models and data-driven models has now found a harmonious middle ground. This convergence has paved the way for significant advancements in the realms of transfer learning and machine-learning-assisted simulation, opening up new avenues for research and practical application.
The paramount objective of data scientists in the oil and gas industry is to decipher hidden patterns within the data and subsequently transform these insights into domain knowledge, which, in turn, guides operation and facilitates educated decision-making. Hence, despite the thrilling advancements in deep learning and large language models, the upstream industry continues to extensively use traditional machine-learning models such as tree-based algorithms for processing structured data sets because of their strong interpretability.
Another prominent trend involves the widespread development and application of no-code or low-code machine-learning platforms, empowering citizen data scientists to efficiently leverage machine-learning models.
As we press forward, we are confronted with a series of challenges, including the following:
- Coping with an abundance of data with low quality that need significant effort in manipulation and quality control
- Contending with the relatively high cost of data acquisition, which curtails the full exploitation of data-hungry models
- Encountering a scarcity of successful proof-of-concept projects that make it to production
- Lacking interpretability of complex but powerful models, hindering our ability to deliver tangible insight to subject-matter experts and management
The selected list of papers shares some interesting examples to showcase the value added by close collaboration between data scientists and subject-matter experts and how the power of the digital revolution actively revitalizes our industry.
Recommended additional reading at OnePetro: www.onepetro.org.
SPE 204712 Data-Driven Solution for Enhancing Workover Intervention Activities by Saniya Karnik, SLB, et al.
SPE 211363 Generation of a Regional Fluid Database for Gas Condensate Assets in a Thrusted Carbonate Environment of the Northern Emirates by Harshil Saradva, Sharjah National Oil Corporation, et al.
SPE 211719 Machine-Learning-Assisted Well-Log Data Quality Control and Preprocessing Lab by Nader Gerges, ADNOC, et al.
Data Analytics Enables Reconstruction of Missing Segments in Well Data History
Authors: Carpenter Chris 
Published: October 2023

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 208137, “Reconstruction of Missing Segments in Well Data History Using Data Analytics,” by Yuanjun Li, SPE, and Roland Horne, SPE, Stanford University, and Ahmed Alshmakhy, SPE, ADNOC, et al. The paper has not been peer reviewed.

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The problem of missing data is a common one in well-production records. An incomplete data set is commonly simplified by omitting all observations with missing values, which can lead to significant information loss. In the complete paper, the authors developed an efficient procedure that enables fast reconstruction of the entire production data set with multiple missing sections in different variables. Ultimately, the complete information can support the reservoir history-matching process and production allocation and can develop models for reservoir performance prediction.

Introduction
Missing values are a prevalent issue in pressure transient analysis. Their occurrence is often unavoidable and is a serious problem because many data-mining algorithms cannot work with data sets that are missing values. Sources of missing field data include operational problems, changes in operating conditions, and variable sampling frequencies.

Imputation Technique of Missing Values
Case Deletion. If a missing data problem exists in a multivariate series, the time series may not be evaluated appropriately. In time-series data, each record is unique. Thus, deleting a record would result in a series with gaps, which would be unusable for many analyses. Furthermore, the time-series plot would be truncated and riddled with flaws. Thus, to conduct a time-series analysis, those missing observations must be estimated and reinserted. When the number of missing values in the data set is considerable, eliminating the associated cases might result in the loss of vital and valuable information. Researchers have found that case elimination is not appropriate when the missing rate is greater than 3%.
Physics-Based Imputation. Any physics-driven diagnostic or model is based on fundamental assumptions that may or may not be correct in all situations but are intended to represent reservoir behavior. While data-driven models do not make any assumptions about underlying physics, they rely on training data. Therefore, data analytics and machine learning can be an ideal substitute.
Local Statistical Approach. Local statistical methods include the K-nearest-neighbor (KNN) imputation and local-least-square imputation, two of the most well-known algorithms. To impute the incomplete data, the KNN imputation employs a pairwise correlation between the target gene with missing values and the K closest reference genes. For missing-value imputation, the techniques in this local method use local similarity patterns in the data sets or a specific column of data.
Deep Learning Enhances Transfer Learning for Tight-Oil Production Prediction
Authors: Carpenter Chris 
Published: October 2023

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 2021-5688, “Physics-Assisted Transfer Learning for Production Prediction in Unconventional Reservoirs,” by J. Cornelio, SPE, S. Mohd Razak, SPE, and A. Jahandideh, University of Southern California, et al. The paper has not been peer reviewed.

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A physics-assisted deep-learning model is presented to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models. The developed model uses a deep-learning architecture to map formation properties to their corresponding production responses using a low-dimensional feature space representation. The results indicate that physics-based simulated data can facilitate production predictions when out-of-range (unseen) input parameters have to extrapolate from data and that transferring the weights learned from the source field to the target field can add valuable information to enhance the prediction performance of the target field.

Introduction
While the limitations of simulation models make them unreliable for developing unconventional reservoirs, the increasing number of wells drilled in recent decades has provided an extensive database for tight oil horizontal wells. The use of such large data sets has permitted the use of data-driven proxy models. These models rely on estimating the approximate functional input/output relations by using various statistical and mathematical techniques. Neural networks are one such type of model designed to emulate neurons in the brain to find patterns within data that have complex relations. Implementation of data-driven proxy models has been used in the characterization of both unconventional and conventional reservoirs. However, these models cannot extrapolate data outside the training-data range.
Transfer learning is one way in which a data-driven model trained on a large data set can be used to learn relevant and common features to solve a specific task with a given source data set. These features are then transferred to build a different model for a target data set with a similar problem definition. The method proposed by the authors attempts to take the benefits of both physics-based simulators and data-driven proxy models to build a more-robust model that can be transferred to build a reliable proxy model for a new field with limited data.
In the model presented in the complete paper, all discrepancies between the simulated responses and field production data can be compensated. Then, the model is transferred to a second neural network designed to be a proxy model for the target field with much smaller data. The neural-network model will attempt to capture the correlations between its formation properties and the difference in production responses from this field. The equations vital to this process are provided in the complete paper.
Automated Work Flow for Sand Prediction, Monitoring Improves Operational Efficiency
Authors: Carpenter Chris 
Published: October 2023

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 22989, “An Automated Work Flow of Simplified Representative Elementary Volume Approach to Sand Prediction and Control Monitoring Using Web-Based Interfaces To Reference With Operational Efficiency Improvement in Zawtika Field, Myanmar,” by Supha-Kitti Dhadachaipathomphong, Prapong Sooksawat, and Pooritat Toprasert, PTTEP, et al. The paper has not been peer reviewed. Copyright 2023 International Petroleum Technology Conference. Reproduced by permission.

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The objective of the study described in the complete paper is to increase operational efficiency by using a sand-prediction model to estimate sand production per well based on current operating conditions and to calculate a sand-operating envelope for each well to guide planning and management. The authors found that the simplified representative elementary volume (SREV) approach to sand prediction and control monitoring (SPCM) resulted in an improvement in operational efficiency by reducing time spent on manual analysis and decision-making through dashboards with predictive results. The reader is encouraged to review the complete paper, which contains detailed work flows that include associated equations and steps.

Study Objective
- Implement SREV approach
- Use a sand-prediction model to estimate sand production per well based on current operating conditions
- Estimate sand accumulation from the wellhead to the inlet separator dynamically
- Predict wall thickness of the main flowlines from the wellhead to the production and test manifold
- Forecast sand production when considering changes in reservoir conditions
- Use an automated work flow to run the network model and obtain flow conditions, generate the well’s sand-prediction model based on well design and reservoir information, and calibrate the well’s sand-production models based on the latest validated test or sensor data
- Implement Web-based interfaces

Study Area
The Zawtika gas field is in the Gulf of Moattama, Myanmar. A total of 123 wells and 11 platforms was used in the study. Some wells suffer from sand production because the sands are unconsolidated or weakly consolidated from Pliocene to Pleistocene geologic sequences. Sand production causes severe problems such as reduced well productivity and gas potential and damage to surface equipment such as choke, pipeline, and production systems.
Self-Ignited People Development Via Technical CoPs - Optimum Engagement Strategies to Foster the Full Potential of Our Local Staff
Authors: Drumond J. D. De Sousa 
Published: October 2023

Abstract
Developing local resources and effectively transferring knowledge from seasoned professional and among local staff continue to be key elements and enablers for continuous creation of value in a transitioning energy sector where sustainability, diversity, equality and inclusion are in the spotlight more than never before. This paper continues the work on SPE-210844-MS and SPE-210961-MS, presenting new actions and verification, showcasing outstanding engagement and sustainability figures from 20 months of continuous activity and data analysis. How the strategy can be re-used and extended Is also presented.
Further integration of the Ecosystem for the C&I (Control & Instrumentation) CoP (Community of Practice) was at the heart of the work. Strong emphasis on the Navigability, Mesh Interconnection between objects, Adaptability, Diversity and Flexibility was given. A point of Attention was introduced to eliminate the "Time Zone Bias", which has been identified as an unintended discriminatory effect of global teams that are located in country sites far between them. The use of Heat-Maps analytics was employed as a verification/validation element that the effect was effectively canceled. More than 50 variables were employed to provide mathematically consistent KPIs.
Quantum leap effects were immediately observable after the changes and enhancements were introduced. The level of engagement and utilization could be verified on several fronts. A few of these results included: 1) An enormous growth in the number of "Unique Active Users" of the Knowledge Transfer platform for the C&I CoP, which gradually and steadily increased by more than 1768 %. 2) A sustained increase of monthly average time invested on Learning and Knowledge-Transfer activities by total monthly Users. 3) Use outside working hours (staff investing their own time on self-ignited development) shows remarkable figures, including Saturday and Sunday use of the CoP´s Knowledge Hub. 4) Monthly utilization level (% of time the C&I CoP was being used out of the 24/7 existing time slots) averaged 72%, even without the need to introduce "Special live knowledge transfer events". 5) Monthly average of "Unique active users", as well as many other interesting figures and finding are explored on the paper.
Data Analytics was employed to ID factors that positively affected the level of sustained utilization of the platform, as well as the level of engagement of "Unique active users". A deep understanding and modeling of multiple operational contexts and working environments of users of the CoP allowed to eliminate potentially discriminating factors. The modelling of competence needs against ongoing activities facilitated to proactively discover temporal Gaps and introduce development objects that effectively tackled those needs.
Sedimentary Evolution of Delta and Reservoir Distribution Under the Control of a Volcanic System
Authors: Hou Dongmei , Li Chao , Gao Pengyu , Yuan Xun , Zhang Xiaolong , Cheng Zhong 
Published: October 2023

Abstract
The BZ34-9 oilfield is a clastic reservoir discovered in the Oligocene strata in the southern part of the Bohai Bay Basin. The volcanic system here is intermixed with a clastic reservoir that spans more than 1000 meters. In its upper part, it overlies about 2000 meters of Miocene strata. Volcanic activity has influenced the deposition and filling of the lacustrine and played a key role in controlling the distribution of clastic reservoirs. Based on 3D seismic data and lithological data from 74 wells, four types of volcanic were identified and extracted according to seismic sequences. Establish and clearly state the reciprocal relationship between volcanism and sedimentation.
The study of volcanic stratigraphic framework reflects the environmental change of volcanoes from terrestrial to marine, which is consistent with the understanding of the sedimentary environment from paleontological and sedimentary facies studies. Small-scale volcanism predates the formation of the early Oligocene strata, and a regional eruption center to the south and east of the BZ34-9 oilfield provides the tectonic setting for the basin evolution. The early Oligocene strata was deposited in a shallow lake environment, thin-bed distributary channels are developed in the near-source facies belt near the central volcano, and thick-bed mouth dams are developed in the far-source facies belt. Volcanoes of this period influenced the distribution of clastic reservoirs by the effusion facies and intrusive facies near conical craters. Water depth becomes deeper when the late Oligocene strata are deposited, thick-bed continuous underwater distributary channels and an estuary dam is developed in the sedimentary area. Volcanic activity was moderate until the last major eruption. During this period, the hydrothermal vent in the far-source facies belt is the main factor affecting the distribution of clastic reservoirs.
It is of great significance for the further fine description of reservoirs and regional exploration.
Expired Medications as Corrosion Inhibitors in the Oil and Gas Industry
Authors: Almubarak T. , AlOtaibi F. , Rafie M. , Baiz A. 
Published: October 2023

Abstract
As environmental awareness is rising worldwide, the oil and gas industry is introducing alternative chemicals in their operation that can degrade after disposal. Yet, there is a bigger concern regarding waste materials that are generated daily and accumulate in landfills with minimal recycling solutions. Research has shown that flushed expired medications leach pharmaceuticals into the environment. Consequently, causing severe harm to plants and aquatic life. This paper explores the possibility of utilizing expired high-use medications as corrosion inhibitors.
To determine the inhibition efficiency of expired medications, low carbon steel metal coupons were exposed to 15 wt.% HCl solutions at temperatures between 77-200°F. The medications have been divided into 3 categories based on their functionality: Category A, Category B, and Category C. Medications from each category were powdered and were used in these tests at a concentration of 2 wt.% for 6 hours. In addition, a control solution containing no corrosion inhibitor was used to establish a corrosion rate for a base case.
Category C medication was observed to perform well at room temperature providing a maximum of 99% corrosion inhibition efficiency. Category A and Category B on the other hand, did not provide as much corrosion inhibition. At a temperature of 150°F, the control solution showed a catastrophic failure producing a corrosion rate of 0.371 lb/ft2, whereas the use of Category C medication was observed to maintain good corrosion inhibition efficiency of 88.3%. When the temperature was raised to 200°F, the corrosion rate observed was 0. 0.0352 lb/ft2 for Category C medication, in the presence of 1 wt.% corrosion inhibitor intensifier. These results pass the oil and gas acceptable low carbon steel corrosion requirements of producing a corrosion rate below 0.05 lb/ft2 for the duration of 6 hours.
The work shows that expired medications can be utilized as corrosion inhibitors. Due to being expired and not fit for human consumption, the cost is inherently low. Thus, these chemicals may provide alternatives to commercial corrosion inhibitors while concurrently reducing damage to the environment.
Flow Profile Evaluation in Producer Wells by Geochemical Studies of Oil and Water and Analysis of Anomalies Associated with Deep Fault Zones
Authors: Shipaeva M. S. , Nurgaliev D. K. , Sudakov V. A. , Shakirov A. A. , Ganiev B. G. , Lutfullin A. A. 
Published: October 2023

Abstract
A major problem in the mature oil fields is the accompanying water production and oil production from different layers. Geochemical signals from different formations and sources can be used for identification flow profile. The study of geochemistry by area allows to establish the migration paths and movement of fluids inside the reservoir. This article focuses on the geochemical composition and isotopic geochemistry of brines and oils, as well as the metal content and the use of fingerprinting for oils (biomarker analysis). It allows to determine: the modes of development of reserves in different parts of the field, the distribution of liquid flows. The main tasks are: identification of geochemical anomalous zones and wells, analysis of the water source in return wells, analysis of communication between geochemical data and cumulative oil recovery. The first step involves selecting candidate wells for sampling. Then reservoir fluid samples are taken, and their unique composition is measured through high-precision mass spectrometric studies (ICP-MS), revealing the content of components to billions of fractions. The "oil fingerprinting" method (GC-MS) is used to identify biomarkers in oil composition and conditions of their origin. Using the developed automated algorithm allows identifying the distinctive characteristics of each reservoir and each phase - water and oil. Graphs and maps are plotted based on the distribution of the identified geoindicators. Geochemical survey was carried out by samples of wellhead samples of more than 100 wells, working for carbonate and terrigenous reservoirs. Area maps of changes in the geochemical background of the field have been constructed. The data of the fluid's geochemical composition from the development objects gave the determining of the inflow profile of each phase in the section of the perforated layers and allowed to compare it with the available geophysical field surveys. The identified difference of oils by the coefficient 2nC29/C28+C30 indicates different stages of hydrocarbons inflow into the traps. In the Devonian system the bright anomalies in the geochemical composition of water and oil were discovered, which are associated with faults and wells with record oil production for the last 50 years, where the actual cumulative production exceeds the volume of the originally estimated recoverable oil. Received data was used in the adaptation of the reservoir simulation model.
Applying Machine Learning NLP Algorithm for Reconciliation Geology and Petrophysics in Rock Typing
Authors: Tveritnev A. , Khanji M. , Abdullah S. , Rojas L. , Ermilov A. , Al Mansoori F. , Alblooshi A. 
Published: October 2023

Oil- and gas-bearing rock deposits have distinct properties that significantly influence fluid distribution in pore spaces and the rock's ability to facilitate fluid flow. Rock typing involves analyzing various subsurface data to understand property relationships, enabling predictions even in data-limited areas. Central to this is understanding porosity, permeability, and saturation, which are crucial for identifying fluid types, volumes, flow rates, and estimating fluid recovery potential. These fundamental properties form the basis for informed decision-making in hydrocarbon reservoir development. While extensive descriptions with significant information exist, the data is frozen in text format and needs integration into analytical solutions like rock typing algorithms.
Applying text analysis, a crucial area in natural language processing, aims to extract meaningful insights and valuable information from unstructured textual data. With the vast amount of text generated every day, automated and efficient text analysis methods are becoming increasingly essential. Machine learning techniques have revolutionized the analysis and understanding of text data. In this paper, we present a comprehensive summary of the available methods for text analysis using machine learning, covering various stages of the process, from data preprocessing to advanced text modeling approaches. The overview explores the strengths and limitations of each method, providing researchers and practitioners with valuable insights for their text analysis endeavors.
A data-driven rock typing scheme is necessary for decision-making and optimization to achieve the best ultimate recovery of hydrocarbons in the most efficient way.