Real Oilfield Research Articles

EFFECTIVE DOSAGE OF CORROSION INHIBITORS FOR OILFIELD EQUIPMENT PROTECTION FROM LOCALIZED CARBON DIOXIDE CORROSION

The article presents the results of laboratory tests of the industrial corrosion inhibitor VITERASOYUZ-2000 of the BP-2 brand (class of imidazolines) used in the oil and gas industry. In 2021, one of the vertically integrated companies purchased 20 thousand tons of this reagent, which is 10 % of the total purchased volume. The volume of corrosion inhibitors purchased by the company annually (~ 200 thousand t) is 97 % a product of domestic manufacturers. The main line of inhibitors purchased in 2021 is represented by the following brands: Sonkor (> 40 thousand t), Azol (~ 40 thousand t), Kormaster (> 30 thousand t), UnoKem (~ 30 thousand t), VITERASOYUZ (~ 20 thousand t), Scimol (> 10 thousand t), ETK (~ 10 thousand t), others (~ 20 thousand t). These reagents were used to inhibit carbon dioxide, hydrogen sulfide and mixed corrosion of oilfield equipment.In order to evaluate the effectiveness of inhibitors in suppressing local carbon dioxide corrosion and determine their effective dosage laboratory studies were performed using a specially developed authors’ technique. A distinctive feature of the technique is the modeling of an essential feature of corrosion processes occurring in real oilfield systems in the presence of carbon dioxide – a high rate of local corrosion exceeding the corrosion rate by 2–9 times. The standard environment and conditions of laboratory experiments used in laboratory tests make it possible to reproduce local corrosion damage visible and measured using a needle micrometer (> 0.01 mm) on corrosion control samples by the weight method. It is shown that the protective effect of the corrosion inhibitor VITERASOYUZ-2000 of the BP-2 brand in relation to local corrosion is achieved at a dosage of 100–150 mg/dm3.The practical significance of the research is shown by a retrospective analysis of data from weight (gravimetric) corrosion monitoring during industrial testing of corrosion inhibitors, which confirm that the use of «traditional» dosages of 15–40 mg/dm3 (based on the total volume of liquid = oil + water) with a high protective effect against corrosion consumption («general corrosion») do not provide a protective effect against local corrosion. It is noted that in some cases, corrosion inhibitors, due to reducing corrosion consumption, can not help only reduce the rate of local corrosion, but, on the contrary, stimulate its development. The target of the tests is to increase the reliability of pipeline operation and environmental safety while reducing the increase in accidents and the number of failures.

Fluorine and boron co doped porous carbon materials as an electrochemical platform in trace-level sodium nitrite sensing

Sodium nitrite (NaNO2) is a common chemical oilfield tracer in the field of reservoir geological analysis. A simple, sensitive, and accurate electrochemical detection method for detection of NaNO2 has been studied. The electrode carbon porous material was based on coal tar pitch n-pentane insoluble extractants (CTPNI) and doped with the elements of fluorine (F) and boron (B) and labeled as FB-CTPNI-PC and an electrochemical sensor modified with FB-CTPNI-PC material was constructed for rapid detection of NaNO2. The characterization results of SEM and TEM indicate that this material has certain number of pore structures, also the BET analysis showed that has main pore size distribution is around the range of 2–20 nm, with abundant mesoporous structure. Moreover, the modified electrode has a good electrocatalytic effect on NaNO2. The electrochemical reaction mechanism of NaNO2 on the surface of the modified electrode was a diffusion-controlled process involving protons. The detection range of NaNO2 can reach 0.1 mM–9.5 mM. When the S/N is 3, the sensitivity is 219.8592 A·L·cm2·mol−1 and the detection limit (LOD) is 0.011 μM. The constructed electrochemical sensor has been applied to the rapid detection of NaNO2 in real oilfield water, with recovery rates ranging from 99.7 % to 103.4 %. It is also roughly consistent with the results of ion chromatography (IC) in detecting NaNO2 in oilfield water, indicating that the sensor has high accuracy. This has provided a new prospect for oil filed tracer and detection technologies.

Integrating μCT imaging of core plugs and transfer learning for automated reservoir rock characterization and tomofacies identification

Traditional methods of core characterization are time-consuming and require the considerable effort of a large number of experts. At the same time, it is quite difficult to readily detect small but important elements in reservoir rocks, especially inside the core volume, that can have a strong impact on assessing reservoir potential and hydrodynamic properties. This research explores the integration of high-resolution micro-computed tomography (μCT) imaging and transfer learning for the automated characterization of reservoir rocks and tomofacies identification. The study involved collecting and marking a dataset containing 66,560 μCT images of 130 standard core plugs containing all major reservoir rock types. Using this dataset of 2D μCT images of core plugs, we applied convolutional neural networks (CNNs) pre-trained on ImageNet and fine-tuned them for rock classification tasks. Applying a transfer learning approach, we trained seven ResNet-50 models, which were able to identify and classify reservoir rock features in μCT images. These features included basic characteristics such as rock type, general texture, the presence of fractures and sulfidization that are common for all reservoir rocks. Further, depending on a specific rock type, individual features such as grain size, individual sedimentological texture and the presence of organic matter zones in the sample could be determined by the specialized model. The results show an average classification accuracy of over 94%, indicating the effectiveness of transfer learning in enhancing rock characterization. The obtained models demonstrated high validation rates and were organized into a workflow for automated rock feature characterization. A case study conducted on a carbonate core from a real oilfield provided a practical demonstration of the automated system's work, whose predictions were validated by traditional analyses such as X-ray diffraction and thin-section petrography. The produced models demonstrated the ability for rapid detection of subtle details that might otherwise have been missed during manual inspection. In addition, the concept of tomofacies resulting from the integration of μCT images and machine learning predictions has proven useful in highlighting different reservoir zones, allowing us to understand their heterogeneity and the ability to separate rock sections despite their apparent monotony. These findings suggest that the integration of μCT imaging and transfer learning is a promising approach for improving the automation and accuracy of reservoir rock characterization. The developed technique represents a significant advance in reservoir characterization, offering rapid and accurate identification of rock properties that can improve exploration and development strategies.

Production optimization in a fractured carbonate reservoir with high producing GOR

The Gas-Oil Ratio (GOR) is a crucial production parameter in oil reservoirs. An increase in GOR results in higher gas production and lower oil production, potentially leading to well shut-ins due to economic infeasibility. This study focuses on a real fractured oil field that requires urgent production operations to reduce the producing GOR. In this study, the static model for the field was developed using commercial software, involving steps such as data collection, fault modeling, meshing, and statistical analysis to prepare for dynamic simulation. The dynamic model incorporates geometry, gridding, and rock properties from the static model, utilizing a dual-porosity approach for the naturally fractured reservoir and the Peng-Robinson equation for fluid phase behavior. Initial reservoir conditions, production history, and rock-fluid interactions were defined, with relative permeability curves indicating a water-wet reservoir and low critical gas saturation affecting the GOR. To better understand the relationship between reservoir and production parameters, a detailed sensitivity analysis was performed using the Response Surface Methodology (RSM). Following the sensitivity analysis, a history matching process was conducted using the Designed Exploration and Controlled Evolution (DECE) optimizer to validate the model for future forecasts. Six operational scenarios were defined to decrease the production GOR and enhance final recovery from the field. The results indicate that the water injection scenario is effective in preventing the GOR increase by maintaining reservoir pressure, thereby sustaining production over a longer period. This scenario also improves oil recovery by approximately 6% compared to the base case. Finally, optimization was carried out using the DECE optimizer for each scenario to fine-tune the operational parameters. The goal was to maximize oil revenue for each scenario during the optimization process. This study stands out as one of the few that provides a comprehensive analysis of production behavior and development planning for a real fractured reservoir with high producing GOR.

Increasing the accuracy of oil recovery factor predictions by integrating lithology data

The object of research in the paper is the process of oil extraction during flooding. The Buckley-Leverett method, which is widely used to estimate oil production in flooding, has certain limitations that lead to uncertainty in the results. This paper proposes to extend the Buckley-Leverett algorithm by integrating lithological data. This approach allows to take into account the influence of geological characteristics of the formation on the process of displacement of oil by water, which leads to a significant increase in the accuracy of forecasting the oil production coefficient. The effectiveness of the proposed method is confirmed on the basis of data analysis of a real oil field. The methodology for calculating the oil recovery coefficient during flooding using lithological dissection is presented. In this work, the steps of determining the oil recovery coefficient were analytically determined, which achieves a certain degree of accuracy due to the inclusion of the lithological characteristics of the permeable zone of the formation. The basic calculation of the lithological distribution over the layer was performed using the Kriging method. To confirm the accuracy of the Buckley-Leverett method, taking into account lithological dissection, the use of data analysis, including an experimental histogram and a theoretical normal distribution plot, is proposed. For data analysis, one hundred cases of lithological distribution were generated using the Sequential Indicator Simulation method. The comparative analysis of the data of the experimental histogram and the theoretical graph of the normal distribution of the determination of oil recovery coefficients by the Buckley-Leverett method for cases with and without lithological dismemberment allows to quantitatively assess the accuracy of both studied options. On the basis of a real oil field, it is shown that the accuracy of oil recovery coefficients by the Buckley-Leverett method, taking into account lithological fragmentation, exceeds the similar method without taking into account lithological fragmentation by 11 %.

Exploring Innovative Applications of Evaporative Cooling for High-Total-Dissolved-Solids Produced-Water Treatment

Summary This research delves into the pioneering application of evaporative cooling (EC) to address the challenge of reducing total dissolved solids (TDS) in produced water generated during hydraulic fracturing operations in the Permian Basin. In this study, we used a meticulously designed laboratory-scale EC system comprising three cooling pads, a fan, a water reservoir, and a pump. Through a systematic series of experiments, both synthetic and authentic produced-water samples were treated, shedding light on the potential of this novel approach. The EC system efficiently processed untreated produced water, circulating it through the cooling pads, all while closely monitoring crucial variables such as inlet and outlet temperatures, relative humidity, and remaining water volume, utilizing a state-of-the-art temperature and humidity meter. Control experiments were systematically conducted to probe the influence of varying salinities, achieved by introducing NaCl into distilled water, encompassing a wide range from 0 ppm to 70,000 ppm. In addition, we extended our evaluation to real produced-water samples collected from diverse regions within the Permian Basin (Delaware, Northern Midland, and Southern Midland), reflecting the system’s capability to manage high salinity and the diverse impurities inherent to oil and gas production. A comparative analysis of energy consumption was undertaken, positioning EC against conventional thermal evaporation techniques. The findings revealed a compelling insight that differences in EC efficiency between synthetic and real oilfield brines were primarily attributed to the presence of sodium (Na+) and chlorine (Cl-) contents rather than the overall TDS concentration. Across all experiments, the system consistently achieved remarkable TDS removal efficiencies, hovering around the 100% mark for both synthetic and authentic produced-water samples. Moreover, the study unveiled a significant advantage of EC, as it proved to be significantly less energy-intensive when juxtaposed with conventional thermal evaporation methods. In addition, our experiments revealed that divalent ions like CaCl2 tend to lower the treatment efficiency compared to monovalent ions, adding a crucial dimension to our understanding of EC in water treatment. The EC system demonstrated remarkable efficiency, achieving nearly 100% TDS removal in both synthetic and real samples while being significantly less energy-intensive than conventional thermal evaporation methods. This research underscores EC’s potential as an effective, sustainable, and economical solution for high-TDS water treatment, with promising applications in industrial settings. The study also draws parallels between EC and air conditioning systems, suggesting its versatility in various industrial applications.

Overview of the Application of Physically Informed Neural Networks to the Problems of Nonlinear Fluid Flow in Porous Media

To describe unsteady multiphase flows in porous media, it is important to consider the non-Newtonian properties of fluids by including rheological laws in the hydrodynamic model. This leads to the formation of a nonlinear system of partial differential equations. To solve this direct problem, it is necessary to linearize the equation system. Algorithm construction for inverse problem solution is problematic since the numerical solution is unstable. The application of implicit methods is reduced to matrix equations with a high rank of the coefficient matrix, which requires significant computational resources. The authors of this paper investigated the possibility of parameterized function (physics-informed neural networks) application to solve direct and inverse problems of non-Newtonian fluid flows in porous media. The results of laboratory experiments to process core samples and field data from a real oil field were selected as examples of application of this method. Due to the lack of analytical solutions, the results obtained via the finite difference method and via real experiments were proposed for validation.

Stochastic lithofacies and petrophysical property modeling for fast history matching in heterogeneous clastic reservoir applications

For complex and multi-layered clastic oil reservoir formations, modeling lithofacies and petrophysical parameters is essential for reservoir characterization, history matching, and uncertainty quantification. This study introduces a real oilfield case study that conducted high-resolution geostatistical modeling of 3D lithofacies and petrophysical properties for rapid and reliable history matching of the Luhais oil reservoir in southern Iraq. For capturing the reservoir's tidal depositional setting using data collected from 47 wells, the lithofacies distribution (sand, shaly sand, and shale) of a 3D geomodel was constructed using sequential indicator simulation (SISIM). Based on the lithofacies modeling results, 50 sets of porosity and permeability distributions were generated using sequential Gaussian simulation (SGSIM) to provide insight into the spatial geological uncertainty and stochastic history matching. For each rock type, distinct variograms were created in the 0° azimuth direction, representing the shoreface line. The standard deviation between every pair of spatial realizations justified the number of variograms employed. An upscaled version of the geomodel, incorporating the lithofacies, permeability, and porosity, was used to construct a reservoir-flow model capable of providing rapid, accurate, and reliable production history matching, including well and field production rates.

Bacterial Cultural Media Containing Lipopeptides for Heavy Oil Recovery Enhancement: The Results of Sand-Packed Column Experiment

Currently, microbial enhanced oil recovery (MEOR) is of great interest because of its potential high efficiency and low environmental impact. Biosurfactants, in the purified form or contained in the bacterial cultural media, are one of the promising directions in MEOR because they are more stable in response to different environmental factors than life microorganisms are. However, the extraction and purification of biosurfactants, as well as their working concentrations and efficacy in real oilfield conditions remain a challenge. In the present work, cultural media of two novel bacterial isolates (Bacillus pumilus and Peribacillus simplex) were used in a model experiment with sand pack columns to enhance the recovery of heavy oil from Romashkino oilfield (Russia). Using FTIR and TLC methods, it was demonstrated that both cultural media contained lipopeptides. In the genome of both bacterial isolates, genes srfAA, fenD and bamC encoding synthesis of surfactin, fengycin, and bacillomycin, respectively, were revealed. The oil recovery efficacy of cell-free cultural media after 24 h of cultivation was 34% higher and 16% lower as compared with synthetic surfactant for B. pumilus and P. simplex, respectively. It can be concluded that the high-cost step of biosurfactants separation and purification may be excluded, and cell free cultural media of the isolates may be directly used in field conditions to enhance the recovery of heavy oils.

A Hybrid Data-Physics Framework for Reservoir Performance Prediction with Application to H2S Production

Summary Model-based reservoir management workflows rely on the ability to generate predictions for large numbers of model and decision scenarios. When suitable simulators or models are not available or cannot be evaluated in a sufficiently short time frame, surrogate modeling techniques can be used instead. In the first part of this paper, we describe extensions of a recently developed open-source framework for creating and training flow network surrogate models, called FlowNet. In particular, we discuss functionality to reproduce historical well rates for wells with arbitrary trajectories, multiple perforated sections, and changing well type or injection phase, as one may encounter in large and complex fields with a long history. Furthermore, we discuss strategies for the placement of additional network nodes in the presence of flow barriers. Despite their flexibility and speed, the applicability of flow network models is limited to phenomena that can be simulated with available numerical simulators. Prediction of poorly understood physics, such as reservoir souring, may require a more data-driven approach. We discuss an extension of the FlowNet framework with a machine learning (ML) proxy for the purpose of generating predictions of H2S production rates. The combined data-physics proxy is trained on historical liquid volume rates, seawater fractions, and H2S production data from a real North Sea oil and gas field, and is then used to generate predictions of H2S production. Several experiments are presented in which the data source, data type, and length of the history are varied. Results indicate that, given a sufficient number of training data, FlowNet is able to produce reliable predictions of conventional oilfield quantities. An experiment performed with the ML proxy suggests that, at least for some production wells, useful predictions of H2S production can be obtained much faster and at much lower computational cost and complexity than would be possible with high-fidelity models. Finally, we discuss some of the current limitations of the approach and options to address them.

Study on formation process and reservoir damage mechanism of blockages caused by polyacrylamide fracturing fluid in production wells

In the process of the large-scale hydraulic fracturing development in oil and gas fields, reservoir damage caused by polymer fracturing fluid is inevitable. The real oil and gas field reservoir conditions make the causes of reservoir damage more complicated, and few reports have provided an in-depth analysis of the composition and structure of actual blockages generated in production wells after hydraulic fracturing. In this work, we innovatively obtained reservoir blockages from production wells after fracturing in oil and gas fields, analyzed their physical and chemical properties by SEM, XRD, IR and other methods, then reproduced the blockages formation process in the laboratory through ampoule aging experiments, and evaluated their impact on the reservoir damage rate and fracture flow capacity by core flow physical simulation experiments. The results indicate that the blockages primarily consist of polyacrylamide and iron ions complexes. For the formation process of blockages, the gel breaker solution will release iron ions after reacting with iron calcite existed in the reservoir. The carboxyl group of polyacrylamides and Fe3+ ions can further form highly stable -(COO)3Fe bonds. The physical simulation results revealed that the generation of Fe3+ ions significantly enhanced the extent of reservoir damage and reduction rate of fracture conductivity associated with polyacrylamide fracturing fluid. In addition, after complete gel breaking, the weak interactions between the short-chain molecular groups and Fe3+ ions result in limited molecular flocculation and entanglement, leading to the reduction in reservoir damage. By identifying the specific formation causes of blockages, we revealed the reservoir damage mechanism from a novel perspective, which is of great significance for guiding the development of low damage fracturing fluid system and further improving oil and gas recovery.

Adaptive spatiotemporal neural networks based on machine learning for missing well-log prediction

The well log is the basis for understanding a geologic structure and evaluating petroleum reservoirs. It inevitably leads to missing logs due to borehole conditions and equipment failures. Existing machine-learning methods introduce convolutional neural networks (CNNs) in temporal networks to learn local morphological (spatial) features for improving prediction. However, they ignore the temporal background information of the logs and the differences in spatial features at different depth points. An adaptive spatiotemporal transformer (ASTT) is developed to effectively overcome these challenges, which consists of a temporal background encoder (TBE), a spatial encoder (SE), and a spatiotemporal decoder (STD). TBE introduces average pooling in the transformer encoder to learn the hidden geologic information in the extracted longitudinal temporal features. SE combines CNN and an attention mechanism to learn the spatial features of each depth point differentially. STD maps the extracted spatiotemporal features to the missing logs. Experimental results on real oilfield data indicate that our ASTT achieves excellent performance in terms of fitting degree and test error. The results in the cross-logs and crosswell cases demonstrate the generalization of ASTT.

Activation behavior of Cu0/FeS/N-graphene derived from waste soybean residue for peroxymonosulfate: Performance and mechanism

Nitrogen-enriched graphene grown on zero valent copper (Cu0) and loaded with iron sulfide (FeS) was prepared through a pyrolysis-liquid phase exfoliation process using waste soybean residue as the precursor biomass. Graphene-based catalyst (GBC) exhibits exceptional proficiency as a peroxymonosulfate activator, displaying a remarkable removal surpassing 90% of total petroleum hydrocarbons (TPHs) in 180 min. Reactive oxygen species were identified through masking experiments and electron paramagnetic resonance analysis in the Cu0/FeS/BC-900/PMS system. S(II) was the source of power for the conversion of Fe(III) to Fe(II), and it was also the reason for the continuous generation of SO4•− in the system. Density functional theory calculations revealed the active sites for adsorption and reaction. The presence of SO4•−, O2•−, and 1O2 species was attributed to the elongation of S–O and O–O bonds, as well as enhancement of electrons in sulfur and sp2 hybrid carbon atoms, respectively. The degradation of TPHs was ascribed to various radical and non-radical pathways of ROSs, with SO4•− serving as the primary oxidizing species. It was fascinating that the Cu0/FeS/BC-900/PMS system exhibited good resistance to inorganic anions and surfactants, and the treatment effect on real oilfield produced wastewater was satisfactory.

New insights in the treatment of real oilfield produced water: Feasibility of adsorption process with coconut husk activated charcoal

Naphthenic acids are water-soluble organic compounds (WSO), present in oilfield produced water (OPW), whose removal aims to comply with environmental legislation and ensure the correct disposal of OPW. In this study, the performance of the adsorptive process for the removal of WSO in synthetic (single- and multi-component) and real OPW was evaluated. It was observed that the lower the pH of the sample, the greater the adsorption capacity of WSO. Moreover, the kinetic parameters of adsorption followed the pseudo-second order model while the equilibrium parameters were represented by the isothermal BET (Brunauer, Emmett and Teller) model. In the desorption process followed the second-order model, and the aqueous solution of sodium hydroxide (0.3 and 0.1 M) showed the highest desorption efficiency (~70 %) in the synthetic and real systems, respectively. With this protocol, the adsorbent proved to be effective for the treatment of OPW, without significant loss of efficiency, for up to three consecutive cycles of adsorption-desorption. In addition, after treatment, the real effluent was classified within the legal parameters for offshore disposal, which enhances its industrial application.

Boosting electro-Fenton performance by constructing a large-scale 3D-architected amorphous–nanocrystalline structure

Metallic glasses (MGs) catalysts have shown promise in Fenton-like reactions for environmental remediation. However, the challenge of balancing catalytic activity and recyclability has hindered their practical application. Here, we present a novel approach to fabricate a three-dimensional (3D) amorphous-nanocrystalline catalyst on Cu-foam using thermal spraying, which shows excellent catalytic activity-recyclability synergy. This method enables large-scale production of a 3D porous MG composite (referred to as 3DP MGC) without the need of post-treatment. The architected 3DP MGC exhibits exceptional electro-Fenton activity, surpassing the performance of most Fe-based catalysts reported to date, with a kinetic rate constant 1.4 times higher than that of the amorphous FeSiB MG ribbon. Furthermore, the 3DP MGC catalyst demonstrates excellent reusability, maintaining efficiency for over 60 cycles without decay. The excellent catalytic activity of the 3D-architected MGC is attributed to the galvanic coupling between the MG and α-Fe phases with a low grain boundary effect and accelerated electronic transfer kinetics, facilitating the generation of multiple reactive oxygen species. Additionally, the catalyst benefits from a sustainably active surface enriched with Fe(II) species, resulting in ultrahigh reusability. Finally, we demonstrate the exceptional environmental tolerance of the architected 3DP MGC catalyst, showcasing its high efficiency in degrading real oilfield wastewater and suggesting its potential for the 3D engineering of MG materials in catalytic applications.

Recent Advances in the Study of In Situ Combustion for Enhanced Oil Recovery

Global estimates for our remaining capacity to exploit developed oil fields indicate that the currently recoverable oil (light oil) will last for approximately 50 years. This necessitates the development of viscous and superviscous oil fields, which will further compensate for the loss of easily produced oil. In situ combustion is the most promising production method, which allows for increased oil recovery from a reservoir. This being the case, this study provides an overview of global trends regarding the research and implementation of the method under consideration, in order to promote understanding of its applicability and effectiveness. The background to the development of the method is discussed in detail, illustrating the growing interest of researchers in its study. Cases of both successful as well as inefficient implementations of this method in real oil fields are considered. The main focus of the article is to investigate the influence of the parent rock and catalysts on the combustion process, as this is a new and actively developing area in the study of enhanced oil recovery using in situ combustion. Geological surveys, in addition to experimental and numerical studies, are considered to be the main methods that are used to investigate processes during in situ combustion. The analysis that we carried out led us to understand that the processes which occur during the combustion of heavy oil are practically unpredictable and, therefore, poorly understood. The specificity of the oil composition under consideration depends on the field, which can lead to a change in the required temperature regimes for its production. This indicates that there exists multiple specific applications for the method under consideration, each requiring additional full studies into both the fractional composition of oil and its reservoirs. The article also considers various technologies for implementing the in situ combustion method, such as ND-ISC, THAITM, COSH, CAGD, and SAGD. However, the literature review has shown that none of the technologies presented is widely used, due to the lack of an evidence base for their successful application in the field. Moreover, it should be noted that this method has no limits associated with the oil occurrence depth. This technology can be implemented in thin reservoirs, as well as in flooded, clayey, sandy, and carbonate reservoirs. The review we have presented can be considered as a guide for further research into the development of global solutions for using the proposed method.

Failure analysis of a high strength Cr-containing tube used in an oil well

With the development of oil and gas resources, the service condition of tube steel becomes more and more severe in oilfields, leading to a large number of corrosion failure cases in actual production. The systematic analysis of the failed parts can help to find failure reasons and propose protection methods. In this paper, the failure analysis of a high-strength tube fractured in a real oilfield was carried out by inspecting the basic properties and corrosion characteristics of the steel. SEM, EDS and XRD were used to analyze the chemical compositions of the products on the surface and around the crack tip. Results indicated that the failure of the tubing steel was due to the stress corrosion cracking under the effects of Cl-, sulfide and stress. Intergranular and pitting corrosion characteristics were also discovered on the failed tube. Microcracks started on the inner wall and propagated to the outer wall. Cl- largely aggregated at both sides of the crack tip, providing a driving force for crack propagation.

Reference synthetic-dataset for novelty detection in oil production data: A perceptive evaluation along with case studies from 51 oilfields

In the last couple of years, several studies were concerned with novelty detection in oil production time-series data to eliminate their deleterious effects. Although many algorithms were investigated over and over, the reported outperforming models are poles apart. Moreover, the reportedly high scores in each study could not be verified individually, which led to uncertainty of algorithms performance in large oilfields. This declared the inevitability of comprising a reference dataset to retain a fair comparison between the existing algorithms and to encourage future development of superior solutions. In this article: we (1) put forward a reference-dataset that consists of 200 synthetic oil wells, derived from UNISIM public datasets, to serve as a benchmark for novelty detection in oilfields, (2) evaluate existing methodologies along with a novel ensemble-learning model on the proposed dataset to serve as a reference point for future research, and (3) validate robustness of the initiated reference-dataset by comparing results on a set of real oilfield data to its results.Apart from that, we investigate the role of statistics in optimizing the selection of outliers cut-off threshold to fulfill a complete automatic approach, which is inaugurated for the first time in oil production data, to the best of our knowledge. Reference dataset and the implemented code are published on GitHub for the intent of re-producibility.

Well Placement Optimization through the Triple-Completion Gas and Downhole Water Sink-Assisted Gravity Drainage (TC-GDWS-AGD) EOR Process

Gas and downhole water sink-assisted gravity drainage (GDWS-AGD) is a new process of enhanced oil recovery (EOR) in oil reservoirs underlain by large bottom aquifers. The process is capital intensive as it requires the construction of dual-completed wells for oil production and water drainage and additional multiple vertical gas-injection wells. The costs could be substantially reduced by eliminating the gas-injection wells and using triple-completed multi-functional wells. These wells are dubbed triple-completion-GDWS-AGD (TC-GDWS-AGD). In this work, we design and optimize the TC-GDWS-AGD oil recovery process in a fictitious oil reservoir (Punq-S3) that emulates a real North Sea oil field. The design aims at maximum oil recovery using a minimum number of triple-completed wells with a gas-injection completion in the vertical section of the well, and two horizontal well sections—the upper section for producing oil (from above the oil/water contact) and the lower section for draining water below the oil/water contact. The three well completions are isolated with hydraulic packers and water is drained from below the oil–water contact using the electric submersible pump. Well placement is optimized using the particle swarm optimization (PSO) technique by considering only 1 or 2 TC-GDWS-AGD wells to maximize a 12-year oil recovery with a minimum volume of produced water. The best well placement was found by considering hundreds of possible well locations throughout the reservoir for the single-well and two-well scenarios. The results show 58% oil recovery and 0.28 water cut for the single-well scenario and 63.5% oil recovery and 0.45 water cut for the two-well scenario. Interestingly, the base-case scenario using two wells without the TC-GDWS-AGD process would give the smallest oil recovery of 55.5% and the largest 70% water cut. The study indicates that the TC-GDWS-AGD process could be more productive by reducing the number of wells and increasing recovery with less water production.

P-wave prediction method under multi-source spatiotemporal feature fusion and physics-informed neural network

Compressional wave velocity (P-wave) plays an important role in seismic amplitude versus offset (AVO) forward and inversion modeling and reservoir prediction. However, due to the high cost and difficulty of P-wave logging, the actual acquired P-wave data is limited, which generally does not suffice for practical applications. To provide reliable data support for oil and gas reservoir exploration, a P-wave prediction method under multi-source spatiotemporal feature fusion and physics-informed neural network (MSTFPIN) is proposed. First, a spatiotemporal hybrid model (STHN) is constructed by combining convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) neural network to accurately express the spatial and temporal characteristics of the logging curves, which utilizes the local perception ability of CNN and long short-term memory function of BLSTM to mine their local shape and depth trends. Then, seismic data are introduced as an additional input of multi-source information, which fills the gap of inter-well geological information, and feature fusion is used to fully exploit the information from multiple sources to enhance the available data. Finally, a physics-informed neural network (PINN) is constructed based on the idea of seismic forward modeling in petroleum engineering to guide the training process of the prediction model, which ensures that the prediction results are consistent with geological laws. P-wave prediction experiments are conducted using real logging and seismic data to verify the effectiveness and generalization ability of the proposed method compared with other methods. The experimental results show that the proposed MSTFPIN model exhibit better prediction performance, and the correlation coefficient and coefficient of determination reach 0.9619 and 0.9234, respectively. Compared with traditional empirical and regression models, the proposed MSTFPIN exhibited higher accuracy and generalization. Overall, our results demonstrate the proposed method can be implemented in real oil fields to improve the accuracy of reservoir prediction.