Large Oil Fields Research Articles

Quick Review

The notable increase in petroleum demand, together with a decline in discovery rates, has highlighted the desire for efficient production of existing oil wells worldwide. Mainly, the productivity of the existing large oil fields makes us consider the principles of managing reservoirs to make the most of extraction. At the same time, many different uncertainties in the course of the developing oil field, including geological, operational, and economic uncertainties, have a detrimental impact on the reservoir's effective production, which is why dealing with uncertainty is crucial for maximizing output. There is a broad variety of studies on managing oil reservoirs under uncertainty information in the literature. In this study a short review of earlier works has been done on optimization strategies and management of uncertainty in reservoir production.

Petroleum++: SPE’s Uses of Artificial Intelligence and Board Meeting Update

In this month’s column, I will share news on the recent SPE Board of Directors meeting in Saudi Arabia and SPE’s utilization of artificial intelligence (AI). The first of the Board’s quarterly meetings of the year is usually held virtually where several administrative items are discussed and the nomination process for the new Board members gets started. The last meeting of the year is held in conjunction with the SPE Annual Technical Conference and Exhibition (ATCE). The middle two meetings are usually held in areas with significant industry operations that gives the Board an opportunity to meet the leaders of SPE sections and regions. The spring 2023 meeting was held for the first time in Dhahran, Saudi Arabia. Below are some of the key decisions reached at the meeting. - Approved the nominating committee’s recommendations for the 2025 SPE President—Olivier Houzé; Data Science and Engineering Analytics Technical Director—Sushma Bhan; Asia Pacific Regional Director—Hazli Sham Kassim; Europe Regional Director—Pierre Emmanuel d’Huart; Latin America and the Caribbean Regional Director—Carlos Alberto Pedroso; North American Regional Director—Kim Oracheski; and the Russia and Caspian Regional Director—Alexey Borisenko. More information about these volunteers was published in the April JPT. - Approved the budget for SPE’s fiscal year 2024, which runs from 1 April 2023 to 31 March 2024. - Reviewed the activities for selling the building in Richardson, Texas. - Updated the process for countries/sections that wish to be considered for an exception to the membership dues. - Removed the requirement for students to provide a full technical paper for participation in the Regional Student Paper Contest. - Approved SPE CCUS International Conference and Exhibition, October 2023, in Al Khobar, Saudi Arabia. - Formed a work group to develop a long-term plan for SPE’s flagship event, ATCE. - Approved SPE policy on AI-generated content in publications (details on the policy are available here). - Updated the Charitable Contribution Policy when SPE donates to areas where our members are affected by natural disasters like hurricanes and earthquakes. In conjunction with the meeting in Dhahran, Board members attended the monthly meeting of the Kingdom of Saudi Arabia Section, with about 500 members, to discuss local and Society-level activities. Saudi Aramco arranged for the Board to visit the Shaybah field operations and the natural wildlife sanctuary in the Rub’ al-Khali, or Empty Quarter. It is interesting how the challenges of operating a large oilfield facility in a remote area like the Empty Quarter are like what I used to see with operations of the Prudhoe Bay and Kuparuk fields in the North Slope of Alaska. It is telling the extent of what we do to provide the world with energy it needs when at airports like in Anchorage next to the counters of United, American, and Alaska Airlines, there were counters for ARCO, BP, and ExxonMobil. The next Board meeting in July 2023 will be held in Kuala Lumpur. I am looking forward to connecting with our colleagues at Petronas as well as other employers and stakeholders in the area.

Saudi Arabia Case Study Illustrates Road to Zero Routine Gas Flaring

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 21182, “The Road to Zero Routine Gas Flaring: A Case Study From Saudi Arabia,” by Majed Alsuwailem, KAPSARC. The paper has not been peer reviewed. Copyright 2021 International Petroleum Technology Conference. Reproduced by permission. _ The complete paper discusses Saudi Arabia’s progress in gas flaring, the measures the government has taken, and how operators have adapted. It also identifies many lessons learned and technological solutions that could be scaled up on a national or a corporate level to reduce gas flaring to achieve zero routine flaring targets, especially in cases where the state owns hydrocarbon assets and leases them to private operators. History of Gas Flaring in Saudi Arabia Following the discovery in 1948 of Ghawar, Saudi Arabia’s largest oil field, oil production in the country increased and more associated gas was produced and flared. The national oil company, Saudi Aramco, had no interest in capturing gas produced from its oil operations. No local or regional market for it existed, and exporting the gas would have required substantial infrastructure investments. This coincided with periods of low crude oil prices that made gas projects uneconomical. However, as the 1960s and ’70s passed, gas came to be increasingly regarded as an essential part of a broader attempt to diversify the Saudi economy. This, in return, allowed the creation of jobs in new frontiers, including the refining and petrochemical industries. In the early 1970s, the Saudi Ministry of Petroleum and Minerals contracted the Texas Eastern Corporation to conduct a technical and economic feasibility study. It evaluated the benefits of establishing a massive refining and petrochemical industry in the city of Jubail on the Arabian Gulf and Yanbu on the Red Sea, with gas as an important feedstock. The study required major capital expenditures on midstream oil operations. Saudi Aramco, on the other hand, proposed exploiting associated gas to generate electricity. Because the price of oil did not exceed $3/bbl before the 1970s, the Saudi government opted for the first option. Saudi Aramco, however, only foresaw an opportunity in this development if the government contributed major capital to the midstream, resulting in a positive outcome for both the stakeholders and the operator, which turned out to be the case. Master Gas System (MGS) The government gave Saudi Aramco a contract to establish the $12 billion MGS to capture, process, and use gas as fuel and feedstock for the petrochemical plants. Most of this project was paid for by the government. By the fall of 1982, the key components of the MGS (gas-gathering and -processing facilities and pipelines) were fully operational. The MGS saved 4.2 billion scf of gas from being flared, which prevented 80 million tonnes of carbon dioxide from being emitted into the atmosphere annually. Over time, the MGS has been expanded as more oil and nonassociated gas fields have been placed onstream and as demand has risen for dry gas in the power sector. By 2018, the MGS had gathered almost 3.5 trillion ft3/yr and is one of the world’s largest single hydrocarbon networks. It includes 4,000 km of pipelines, 50 gas/oil separation plants (GOSPs), seven gas plants, and two natural gas/liquid units.

Technology Focus: Heavy Oil (April 2023)

_ Heavy oils are characterized by high-density, high-viscosity, and high-heavy-fraction components. One of the more common characteristics of heavy oil reservoirs is a low primary recovery factor, which is mainly because of unfavorable mobility ratios between oil and water, negligible solution drives, and faster decline of reservoir pressures because of relatively low oil compressibility. Most of the technologies that apply to heavy oil reservoirs need to address the mobility ratio or viscous forces before any flooding. Given the complex nature of heavy oil reservoirs, paper SPE 209328 present a comprehensive review of the latest technologies and work flows developed for heavy oil reservoir management. Although secondary oil production methods such as waterflooding, water-alternating-gas injection, and gas drive might not be efficient in recovery from heavy oil reservoirs, the applicability and success of these methods can be analyzed on a reservoir basis. The offtake and throughput management philosophy for heavy oil waterflooding is not the same as for classical light oil, and it could play a major role in recovery efficiency. The key point is to understand the nature of heavy oil and reservoir heterogeneity regarding their fluid and rock properties. Recent technology advances in hardware and software have enabled the industry with tools that allow achieving full-field high-resolution simulation modeling; therefore, performing a realistic reservoir simulation is achievable. This might decrease the loss of millions of dollars before field applications. Paper SPE 210185 presents an example of taking advantage of recent simulation technologies and presents a method and work flow under subsurface uncertainty for a large heavy oil field with complex geological characteristics, a long production history, and many wells. The work-flow design is applicable for other fields with similar characteristics and delivery objectives. Paper SPE 203783 discusses the results of a recovery-optimization study conducted in a heavy oil field in south Oman aimed at improving the understanding of deep bottom-up water injection. Recommended additional reading SPE 205449 First Successful Controlled Dumpflood in Deepwater Gulf of Mexico Results in Promising Incremental Rate and Recovery by Bilal A. Hakim, Talos Energy, et al. URTeC 2021-5077 Heavy Oil Polymer Enhanced Oil Recovery in the Challenging Alaskan Arctic—It Works! by A. Dandekar, University of Alaska Fairbanks, et al. SPE 212387 Evaluation of the Oil Recovery and Economic Benefit of the First-Ever Polymer flood Field Pilot To Enhance the Recovery of Heavy Oils on Alaska’s North Slope Using Machine-Assisted Reservoir Simulation by Cody Douglas Keith, University of Alaska Fairbanks

Theoretical and technical progress in exploration practice of the deep-water large oil fields, Santos Basin, Brazil

The history and results of petroleum exploration in the Santos Basin, Brazil are reviewed. The regularity of hydrocarbon enrichment and the key exploration technologies are summarized and analyzed using the seismic, gravity, magnetic and drilling data. It is proposed that the Santos Basin had a structural pattern of two uplifts and three depressions and the Aram-Uirapuru uplift belt controlled the hydrocarbon accumulation. It is believed that the main hydrocarbon source kitchen in the rift period controlled the hydrocarbon-enriched zones, paleo-structures controlled the scale and quality of lacustrine carbonate reservoirs, and continuous thick salt rocks controlled the hydrocarbon formation and preservation. The process and mechanism of reservoirs being transformed by CO2 charging were revealed. Five key exploration technologies were developed, including the variable-velocity mapping for layer-controlled facies-controlled pre-salt structures, the prediction of lacustrine carbonate reservoirs, the prediction of intrusive/effusive rock distribution, the detection of hydrocarbons in lacustrine carbonates, and the logging identification of supercritical CO2 fluid. These theoretical recognitions and exploration technologies have contributed to the discovery of deep-water super-large reservoirs under CNODC projects in Brazil, and will guide the further exploration of deep-water large reservoirs in the Santos Basin and other similar regions.

Application of Computer Vision in Machine Learning-Based Diagnosis of Water Production Mechanisms in Oil Wells

Summary Diagnostic plots, introduced by K. S. Chan, are widely used to determine excessive water production mechanisms. In this paper, we introduce a computer vision model that is capable of segmenting and identifying multiple Chan signatures per plot, for the sake of surveillance and early screening, given that wells could exhibit diverse mechanisms throughout their life cycle. As deep learning (DL) demands a vast amount of information, we start our workflow by building a data set of 10,000 publicly available oil wells that have experienced varying water production mechanisms. Next, we perform preprocessing and remove anomalies from production data, which could be deceptive in analysis. Then, we visualize Chan plots as images and annotate them. Thereafter, we split data set, carry out augmentation, and put data together to be used as input for a convolutional neural network (CNN) layer. Eventually, data are trained utilizing you only look once (YOLO)—a one-stage object detector, hyperparameters are tuned, and model performance is evaluated using mean average precision (mAP). The collected data from fields in Alaska and North Dakota represent oil wells that have been producing for decades. When working with wells that possess noisy production data, we recognized challenge, bias, and tedium in human interpretation of Chan plots. Subsequently, we observed the inevitability of cleaning well production data before constructing the plots and thoroughly revealed its effect on enhancing model potentiality to get a fair score. In addition, we concluded that following a systematic approach of active learning, a technique that allows user to analyze mistakes of algorithm predictions and label data accordingly, accomplished a significant boost in model performance, especially with underrepresented classes. The proposed CNN model, which uses automatic feature extraction and expresses data in detail, is presumed to be robust as it successfully predicted multiple mechanisms of excessive water production, with confidence scores higher than 80%, in wells that exhibit different production conditions such as horizontal trajectories, artificial lift, waterflooding, stimulation, and other well intervention events. In this work, we introduce a novel computer vision model, which combines image processing and DL techniques to identify multiple water production signatures that a well can undergo and eliminate subjectivity of human interpretation. This approach has the potential to be effective, as a part of workflow automation, in expeditious surveillance of large oil fields. Source code is available on GitHub for public use.

Crude oil production prediction based on an intelligent hybrid modelling structure generated by using the clustering algorithm in big data

Since the behavior of a complex dynamic system for a large oil field in Iraq is significantly influenced by many nonlinearities, its dependent parameters exhibit non-stationary with a very high delay time. Developing white-box modelling approaches for such dynamic oil well production cannot handle these large data sets with all dependent dimensions and their non-linear effects. Therefore, this study adopts the hybrid model that combines white-box and black-box to address such problems because the model outputs require various variable types to achieve optimal fitness to measured values. The hybrid model structure needs to evolve with changes in the physical parameters (white-box part) and Neural Networks' Weights (black-box part). The model structure of the proposed hybrid network relied on converting fuzzy rules in a Takagi–Sugeno–Kang Fuzzy System (TSK-FS) into a multilayer perceptron network (MLP). The hybrid parameters are formulated concerning six-dimensional dependent variables to describe them in matrix form or layer and by which can quantify total model outputs. After mapping categorical variables to tuples of MLP, the Gauss-Newton regression (GNR) provides an optimal update of the hybrid parameters to get the best fitting of the model outputs with the target of the dataset. The clustering technique and GNR promote predictive performance due to reducing uncertainties in the hybrid parameters. Due to time being the most effective of the independent variables for predicting oil production, datasets are classified into different clusters based on time. The actual field dataset for training and validation is collected from Zubair Oil Field (9 oil wells), which is implemented to build the proposed model. The results of the hybrid model indicate that the development of the proposed structure has achieved the high capability to represent such big data which is the most imperative feature of the proposed model. Furthermore, obtained results show its accuracy far outpacing competitors and achieving a significant improvement in predictive performance.

Application of multiphase flow fiber optic fluid production profile testing technology in W1 well

In recent years, the number of completions of tight oil horizontal wells in oil field X has increased year by year. Horizontal well fracturing technology has been widely used in the development of tight oil reservoirs in oil field X. However, there are still some technical difficulties in the dynamic monitoring of horizontal wells. Monitoring is mainly for inclined vertical wells, and there are few dynamic monitoring methods and means for horizontal wells. Therefore, the production of each cluster after fracturing in horizontal wells has not been accurately monitored. To obtain the output of each cluster after fracturing in horizontal wells, analyze and evaluate fracturing effects, and guide the preparation of fracturing plans, it is necessary to conduct fiber-optic fluid production profile testing and research. Optical fiber monitoring technology for horizontal wells has developed particularly rapidly in recent years. At present, a relatively mature distributed optical fiber temperature DTS supporting technology has been formed. It is widely used in the monitoring of unconventional reservoirs in some large oil fields abroad, meeting the actual monitoring needs, and has achieved good results. better application effect. This paper expounds the basic principle and well selection principle of horizontal well optical fiber testing, and introduces the knowledge obtained in practical application. It is of great significance to accelerate the application of horizontal well optical fiber monitoring technology in the development of tight oil reservoirs in X oil field, improve the dynamic monitoring of horizontal wells and evaluate the fracturing effect.

Geological model and prospects of oil and gas bearing deposits of the silurian complex of the Pechora sea shelf

The task of this work is the desire of the authors to draw attention to the problem of ac-celerating the pace of searches for promising Arctic objects on the shelf of the Pechora Sea and additional exploration of already discovered deposits in the Silurian oil and gas complex on the territory of the Varandei-Adzva structural zone (VASZ). The main result of the previously per-formed offshore exploration work is the identification of a new oil-bearing area in the eastern part of the Pechoromorsky shelf. Large oil fields have been discovered here: Prirazlomnoye, Varandey-sea, Medynskoye-sea, Dolginskoye. The proven reserves and resources of hydrocarbons (HC) allow us to assume that in the near future a new oil-producing area will be formed on the shelf of the Pechora Sea. In total, the initial raw oil resources in the Pechora Sea are more than 3500 mln tons. Keywords: Pechora sea; silurian oil and gas complex; silurian; oil.

Analysis on causes and influencing factors of sudden death on job of workers in a large oil field branch plant during 2014-2020

Objective: To investigate the epidemiological characteristics of sudden death on job among workers in the large oil field, and to provide scientific basis for dealing with such incidents. Methods: In April 2021, the medical records of employees who died in a large oilfield from 2014 to 2020, and the occupational health examination data in the first year of life were collected, and 85 employees who died suddenly on duty were included in the study. According to whether the employees are exposed to the occupational disease hazards, they are divided into the injured type of work (66 persons) and the non injured type of work (19 persons) . The characteristics of the clinical data of the sudden death employees are analyzed retrospectively. The gender, age, length of service, type of work, distribution of causes of sudden death of the cases are analyzed. The detection of abnormalities in various occupational health examination indicators is analyzed. The chi square test is used to analyze the distribution of the types of work, length of service and abnormal physical examination indicators. Results: Among the 85 employees who died suddenly on duty, the ratio of men and women was 16∶1. The proportion of sudden death among employees aged 40 to 50 years was the highest (54.12%, 46/85) , which was mainly the first-line workers in the affected departments (43.53%, 37/85) and those with 20-30 years of service (57.64%, 49/85) . The main cause of sudden death was cardiogenic sudden death (78.82%, 67/85) . There were statistically significant differences in abnormal rates of blood pressure and blood glucose among workers of different types of work (χ(2)=7.24, 24.22, P<0.05) , and there were statistically significant differences in abnormal rates of blood lipid and blood glucose among workers of different ages of service (χ(2)=12.37, 31.44, P<0.05) Conclusion: Higher risks of sudden death on job are male, older than 40 years old, front-line workers in disaster receiving departments, worked for more than 30 years, and have abnormal cardiovascular indicators. Workers with these high risks are the major target population for the prevention and treatment of sudden death on the job. It's necessary to supervise enterprises to implement protective measures against risk factors, and to strengthen health education and reduce the incidence of cardiovascular and cerebrovascular diseases, in order to reduce the occurrence rate of sudden death in oilfield workers.

Breakthrough in drilling and completion equipment and key technologies for 8000m-class ultra-deep wells onshore China

With the exploration and development of a group of deep ultra-deep large oil and gas fields in Tarim basin and Sichuan Basin, deep oil and gas reservoirs will be the main productivity replacement area and the main exploration focus area. Drilling and completion still face severe challenges such as high temperature and high pressure, complex pressure systems, complex accidents and poor drillability. This paper describes the technology of domestic oil and gas fields with 8000m level drilling and completion. Considering the demand of exploration and development of local oil and gas fields while standardizing the international advanced level, the insufficiency of technology and equipment improvement demand of each block are discussed, and the research direction of technology and equipment is prospected.

The role of the Absheron facies in the formation of the productive strata in the South Caspian Basin

In oilfield practice, the lithofacies criterion is very often decisive for the formation of deposits of liquid and gaseous hydrocarbons. The Absheron lithofacies type of sediments (Absheron facies), enriched in quartz material, composes the reservoirs of the horizons of all large oil and gas fields in the South Caspian Basin. The genesis of the Absheron facies is closely related to the genesis of the productive stratum, there is no consensus on both issues to date. It is indisputable that the productive stratum and the Absheron facies were formed in the conditions of a closed continental basin, which arose as a result of the orogenic restructuring of the areas surrounding the Caspian Sea, the bowing of its bottom and the subsequent Pontian regression of the sea. As a result of the restructuring of the hydrographic system in the south of the Russian Platform, many paleorivers, flowing along the newly formed land, began to flow and carry a huge amount of water and precipitation into the South Caspian Basin. Deposition of the productive stratum and Absheron facies began. The greatest role in the supply of quartz-enriched sandy material belonged to the Paleovolga, which, together with other paleorivers in the north of the South Caspian Basin, formed an extensive marginal delta, from which sediments were transported far to the south, west, and east.

Optimized feature selection assists lithofacies machine learning with sparse well log data combined with calculated attributes in a gradational fluvial sequence

Machine learning (ML) to predict lithofacies from sparse suites of well-log data is difficult in laterally and vertically heterogeneous reservoir formations in oil and gas fields. Meandering, braided fluviatile depositional environments tend to form clastic sequences with laterally discontinuous layers due to the continuous shifting of relatively narrow sandstone channels. Three cored wellbores drilled through such a reservoir in a large oil field, with just four recorded well logs available, are used to classify four lithofacies using ML models. To augment the well-log data, six derivative and volatility attributes were calculated from the recorded gamma ray and density logs, providing sixteen log features for the ML models to select from. A novel, multiple-optimizer feature selection technique was developed to identify high-performing feature combinations with which seven ML models were used to predict lithofacies assisted by multi-k-fold cross validation. Feature combinations with just seven to nine selected log features achieved overall ML lithofacies accuracy of 0.87 for two wells used for training and validation. When the trained ML models were applied to a third well for testing, lithofacies ML prediction accuracy declined to 0.65 for the best performing extreme gradient boosting model with seven features. However, an accuracy of ∼0.76 was achieved by that model in predicting the presence of the pay bearing sandstone and siltstone lithofacies in the test well. A model using only the four recorded well logs was only able to predict the pay-bearing lithofacies with ∼0.6 accuracy. Annotated confusion matrices and feature importance analysis provide additional insight to ML model performance and identify the log attributes that are most influential in enhancing lithofacies prediction.

Analysis of the world oil and gas exploration situation in 2021

The global exploration investment, new oil and gas discoveries, exploration business adjustment strategies of oil companies in 2021, and future favorable exploration domains are systematically analyzed using commercial databases such as IHS and public information of oil companies. It has been found that the world oil and gas exploration situation in 2021 has continued the downturn since the outbreak of COVID-19. The investment and drilling workload decreased slightly, but the success rate of exploration wells, especially deepwater exploration wells, increased significantly, and the newly discovered reserves increased slightly compared with last year. Deep waters of the passive continental margin basins are still the leading sites for discovering conventional large and medium-sized oil and gas fields. The conventional oil and gas exploration in deep formations of onshore petroliferous basins has been keeping a good state, with tight/shale oil and gas discoveries made in Saudi Arabia, Russia, and other countries. While strengthening the exploration and development of local resources, national, international, and independent oil companies have been focusing on major overseas frontiers using their advantages, including risk exploration in deep waters and natural gas. Future favorable exploration directions in the three major frontiers, the global deep waters, deep onshore formations, and unconventional resources, have been clarified. Four suggestions are put forward for the global exploration business of Chinese oil companies: first, a farm in global deepwater frontier basins in advance through bidding at a low cost and adopt the “dual exploration model” after making large-scale discoveries; second, enter new blocks of emerging hot basins in the world through farm-in and other ways, to find large oil and gas fields quickly; third, cooperate with national oil companies of the resource host countries in the form of joint research and actively participate exploration of deep onshore formations of petroliferous basins; fourth, track tight/shale oil and gas cooperation opportunities in a few countries such as Saudi Arabia and Russia, and take advantage of mature domestic theories and technologies to farm in at an appropriate time.

Study of Light and Heavy Minerals and Their Effect on Oil Aggregation in the Zubair Formation for the Rumaila Oil Field in Southern Iraq

Zubair Formation is the most productive reservoir in southern Iraq, which is comprised of sandstones, interbedded with shale sequences and sometimes carbonate rock. It is an important formation in the lower Cretaceous cycle in Iraq. Rumaila oil field is the largest oil field in Iraq and the 6th in the world. Two wells were studied for three depths, one in the southern Rumaila and the other in the north. The study focused on light and heavy minerals in sand fractions and their relationship with hydrocarbon assemblages. For the survey to be complete, the sedimentological study of the cores was also conducted. This research aims to determine the effect of the amount of heavy and light minerals on the generation and production of oil in the reservoir layers. The sedimentological results were the prevalence of sand for all depths, especially in Southern Rumaila. The sand ratio ranged from 62% to 98%. The rate of heavy minerals in southern Rumaila was greater than in Northern on average at 1.17%. Light minerals were the most in Northern Rumaila up to 99.3% represented in quartz with a high percentage and a little part of feldspar represented in orthoclase. Heavy minerals were represented in Opaque (24%), zircon (27.6%), tourmaline (18%), rutile (11%), biotite (3%), chlorite (3.6%), muscovite (3.6%), staurolite (2%) and hornblende (3.6%). The proportion of quartz was the most in light minerals. The average of minerals is ultra-stable, and Opaque is the most dominant over the other minerals. Ultra-stable minerals in the sand that contain hydrocarbon assemblage are an index to mineralogical maturity for sandstone rocks. The SEM images showed deformations in quartz grains due to the high energy; on the other hand, the roundness and sphericity of the grains were evident, especially in Southern Rumaila sands.

Performance evaluation of boosting machine learning algorithms for lithofacies classification in heterogeneous carbonate reservoirs

Lithofacies classification from well logs recorded through heterogeneous carbonate reservoirs helps to improve reservoir discrimination with respect to fluid flow and storage capabilities. A novel technique is developed to classify carbonate lithofacies with the assistance of boosting algorithms for a well drilled into the large Majnoon oil and gas field of southern Iraq. Five boosting machine learning algorithms: Logistic Boosting Regression (LogitBoost), Generalized Boosting Modeling (GBM), Extreme Gradient Boosting (XGBoost), Adaptive Boosting Model (AdaBoost), and K-nearest neighbor (KNN) were comparatively implemented to attain the most realistic lithofacies predictions. Data from available cores provide actual lithofacies determination. Additionally, seven standard well logs recorded provide continuous additional information across the complete reservoir section. Random sub-sampling of the well-log dataset was used to train and test the models. Confusion matrices of the results provided Total Percent Correct (TPC) accuracy measures for LogitBoost, GBM, XGBoost, AdaBoost, and KNN with respect to the entire dataset of 97.65%, 96.99%, 97.74%, 97.74%, and 96.74%, respectively, and with respect to the validation subsets TPC was 94%, 96%, 98%, 91%, and 96%, respectively. The XGBoost provided more accurate lithofacies classification than the other five boosting algorithms with respect to the predictions generated for both the entire dataset and testing subset. The lithofacies predictions were then compared with poro-perm interpretations from the Nuclear Magnetic Resonance (NMR) log and oil rate from the Production Logging Tool (PLT). Rudist-rich and argillaceous lithofacies display lower porosity and permeability, respectively. The Rudist-rich lithotype is associated with high-production-rate zones, whereas the argillaceous facies is associated with low-production-rate zones. The boosting machine learning workflow developed efficiently provides accurate lithofacies classification with reduced uncertainty in carbonate lithofacies determinations. The entire workflow was fully implemented and visualized using open-source R computer codes. These codes have the potential to be generalized for the application of facies classification to other clastic and carbonate reservoirs in oil and gas fields.

Friendly Assistance and Self-Reliance

Abstract Advanced geophysical techniques developed in Hungary contributed immensely to the success of geological surveys carried out in the PRC after 1956. A group of Hungarian geophysicists played a pivotal role in discovering and exploring oil deposits in the Songliao Basin, which later became known as Daqing, the largest oil field in China. Based on declassified primary sources from Hungarian archives and firsthand testimony from people involved in the expedition, this article examines how Sino-Hungarian cooperation in Cold War activities evolved against the backdrop of the radicalization of Chinese politics and growing tensions between the Soviet Union and China. Taking the Hungarian geophysicists’ expedition as an example, the article explores the historical setting of early exchanges and the daily practice of scientific and technological interactions between the PRC and one of the closest East European allies of the Soviet Union.

Editor's Note

Editor's Note

The Fault Effects on the Oil Migration in the Ultra-Deep Fuman Oilfield of the Tarim Basin, NW China

A giant, ultra-deep (&gt;7000 m) strike-slip fault-related oilfield has been found in the central depression of the Tarim Basin. However, little research has addressed this discovery to understand the fault effects on the oil migration, which constrains the oil exploitation deployment and well optimization. Based on fault modeling and fluid analysis, we present the effects of fault segment on the oil segmentation and migration in the ultra-deep Fuman Oilfield. The results show a distinct fault segmentation and also subsequent variable fault architecture and non-connected fracture network other than a through-going fault zone. There are fault segment-related isolated fractured reservoirs and fluid variation along the fault strike, which indicates that a large oilfield comprises a series of unconnected small oil reservoirs along the fault segments. The fluid segmentation and molecular indicator of dibenzothiophenes reveal that there is a variable petroleum lateral migration along the strike-slip fault segment. The fault segmentation is a major contribution in the varied oil accumulation along the strike-slip fault zone in Fuman Oilfield. This case study suggests that fault segmentation has variable effects on petroleum migration and accumulation along the strike-slip zone.

Experiment-based coupled dynamic analysis of submerged production platform-mooring system in internal solitary waves

As a competitive alternative to the conventional dry tree and wet tree concepts, the next generation subsea production system, with a focus on the critical issues of well access and riser design, is designed to utilize shallow-water rated subsea production systems for the development of large oil and gas fields in deep and ultra-deep water. This study focuses on the crucial component of the next generation subsea production system, referred to as the submerged production platform-mooring system, and investigates its nonlinear coupled dynamic responses to internal solitary waves. A time-domain coupled dynamic analysis method is developed. Specifically, based on the load experiment in a large gravitationally stratified fluid flume, the applicability conditions of internal solitary wave theories and the determination method of hydrodynamic coefficients are quantitatively characterized, and the internal solitary wave load model for the submerged production platform is developed and validated. The fully coupled dynamic analysis model for the submerged production platform-mooring system is established and solved by the Generalized-α integration algorithm in combination with the Newton iterative method. The coupling effects, dynamic response characteristics, and security weaknesses of the submerged production platform-mooring system are analyzed. The effects of various key factors on the dynamic responses of the submerged production platform-mooring system are systematically analyzed. The results demonstrate that the dynamic responses of the submerged production platform-mooring system to internal solitary waves are characterized by the large amplitude surge and heave motions, and the significant fluctuation in the mooring tension, particularly when the submerged production platform is located at the density pycnocline. The developed method also has its applicability to the dynamic analysis of the deepwater free standing hybrid riser system in internal solitary waves.