Intelligent Petroleum Engineering

Data-driven approaches and artificial intelligence(AI)algorithms are promising enough to be relied on even more than physics-based methods;their main feed is data which is the fundamental element of each phenomenon.These algorithms learn from data and unveil unseen patterns out of it The petroleum industry as a realm where huge volumes of data are generated every second is of great interest to this new technology.As the oil and gas industry is in the transition phase to oilfield digitization,there has been an increased drive to integrate data-driven modeling and machine learning(ML)algorithms in different petroleum engineering challenges.ML has been widely used in different areas of the industry.Many extensive studies have been devoted to exploring AI applicability in various disciplines of this industry;however,lack of two main features is noticeable.Most of the research is either not practical enough to be applicable in real-field challenges or limited to a specific problem and not generalizable.Attention must be given to data itself and the way it is classified and stored.Although there are sheer volumes of data coming from different disciplines,they reside in departmental silos and are not accessible by consumers.In order to derive as much insight as possible out of data,the data needs to be stored in a centralized repository from where the data can be readily consumed by different applications.

Current Situation and Future of Petroleum Engineering Technology Service in China

Petroleum engineering service is one of the pillars that support the petroleum industry in China. Being one of CNPC’s main businesses, the sector has always been escorting the Group to realize its strategic goals. Since a new round of specialized re-structuring in 2007, the sector has been promoting all its

Current Situation and Future of Petroleum Engineering Technology Service in China

Major bottlenecks in the development of the sector Compared with foreign peers, the sector still lags behind in capacity and performance, reflecting the sector needs to do more in improving technology innovation ability, setting up favorable mechanism and investing more in technology research. The current situation indicates that the following factors have been affecting the development of the sector.

Erratum to“Probing the Interfacial Forces and Surface Interaction Mechanisms in Petroleum Production Processes”[Engineering 18(2022)49-61]

In Ref.[1],Eq.(8)has a typo,the following replacement should be done.γ_(bb)/2rδ/δr(rδ(r,t)/δr=2γ_(bb)/R_(bb)-Þ(t,t)-Ⅱ[h[r,t)](bubble=drop-bubble=drop)The publisher regrets an error in the original–article,and the sentence that explained the equation“Eqs.(8)–(10)show the augmented Young–Laplace equation for the interactions of gas bubbles or liquid droplets in different configurations,where Rb is the bubble/drop radius,Rp is the particle radius,Rbp=(1/Rb+1/Rp)1.

Analysis of the Applicability of a Risk Quantitative Evaluation Method to High Temperature-Pressure Drilling Engineering

The optimization of methods for the quantitative evaluation of risks in drilling engineering is an effective means to ensure safety in situations where high temperature and high pressure blocks are considered.In such a context,this study analyzes the complexity of the drilled wells in such blocks.It is shown that phenomena such as well kick,loss,circulation,and sticking,are related to the imbalance of wellbore pressure.A method for risk quantitative evaluation is proposed accordingly.The method is used to evaluate the risk for 9 drilled wells.By comparing the predictions of the method with actual historical data related to these wells,it is found that the coincidence rate is about 95%.

LDH-based nanomaterials for photocatalytic applications:A comprehensive review on the role of bi/trivalent cations, anions,morphology, defect engineering, memory effect, and heterojunction formation

Using sunlight to drive chemical reactions via photocatalysis is paramount for a sustainable future.Among several photocatalysts,employing layered double hydrides(LDH) for photocatalytic application is most straightforward and desirable owing to their distinctive two-dimensional(2D) lamellar structure and optical attributes.This article reviews the advancements in bimetallic/trimetallic LDHs and various strategies to achieve high efficiency toward an outstanding performing photocatalyst.Firstly,the tuning of LDH components that control the electro nic and structural properties is explained.The tu ning obtained through the adoption,combination,and incorporation of different cations and anions is also explained.The progress of modification methods,such as the adoption of different morphologies,delamination,and defect engineering towards enhanced photocatalytic activities,is discussed in the mainstream.The band engineering,structural characteristics,and redox tuning are further deliberated to maximize solar energy harvesting for different photocatalytic applications.Finally,the progress obtained in forming hierarchical heterostructures through hybridization with other semiconductors or conducting materials is systematically disclosed to get maximum photocatalytic performance.Moreover,the structural changes during the in-situ synthesis of LDH and the stability of LDH-based photocatalysts are deliberated.The review also summarizes the improvements in LDH properties obtained through modification tactics and discusses the prospects for future energy and environmental applications.

Physics-informed neural network-based petroleum reservoir simulation with sparse data using domain decomposition

Recent advances in deep learning have expanded new possibilities for fluid flow simulation in petroleum reservoirs.However,the predominant approach in existing research is to train neural networks using high-fidelity numerical simulation data.This presents a significant challenge because the sole source of authentic wellbore production data for training is sparse.In response to this challenge,this work introduces a novel architecture called physics-informed neural network based on domain decomposition(PINN-DD),aiming to effectively utilize the sparse production data of wells for reservoir simulation with large-scale systems.To harness the capabilities of physics-informed neural networks(PINNs)in handling small-scale spatial-temporal domain while addressing the challenges of large-scale systems with sparse labeled data,the computational domain is divided into two distinct sub-domains:the well-containing and the well-free sub-domain.Moreover,the two sub-domains and the interface are rigorously constrained by the governing equations,data matching,and boundary conditions.The accuracy of the proposed method is evaluated on two problems,and its performance is compared against state-of-the-art PINNs through numerical analysis as a benchmark.The results demonstrate the superiority of PINN-DD in handling large-scale reservoir simulation with limited data and show its potential to outperform conventional PINNs in such scenarios.

A synthetical geoengineering approach to evaluate the largest hydraulic fracturing-induced earthquake in the East Shale Basin, Alberta

On 2019-03-04,the largest induced earthquake(ML4.18)occurred in the East Shale Basin,Alberta,and the underlying physical mechanisms have not been fully understood.This paper proposes a synthetical geoengineering methodology to comprehensively characterize this earthquake caused by hydraulic fracturing.Based on 3D structural,petrophysical,and geomechanical models,an unconventional fracture model is constructed by considering the stress shadow between adjacent hydraulic fractures and the interactions between hydraulic and natural fractures.Coupled poroelastic simulations are conducted to reveal the triggering mechanisms of induced seismicity.It is found that four vertical basement-rooted faults were identified via focal mechanisms analysis.The brittleness index(BI)along two horizontal wells has a high magnitude(BI>0.5),indicating the potential susceptibility of rock brittleness.Due to the presence of overpressure,pre-existing faults in the Duvernay Formation are highly susceptible to fault reactivation.The occurrence of the earthquake clusters has been attributed to the fracturing fluid injection during the west 38^(th)-39^(th) stage and east 38^(th) stage completions.Rock brittleness,formation overpressure,and large fracturing job size account for the nucleation of earthquake clusters,and unconventional natural-hydraulic fracture networks provide fluid flow pathways to cause fault reactivation.This workflow can be used to mitigate potential seismic risks in unconventional reservoirs in other fields.

The Evaluation of Liquefied Petroleum Gas (LPG) Utilization as an Alternative Automobile Fuel in Nigeria

The utilization of liquefied petroleum gas (LPG) as an alternative automobile fuel in Nigeria was studied, focusing on varying different blend ratios of propane and butane as an alternative fuel in a single-cylinder, four-stroke, and spark ignition (SI) engine. Ricardo WAVE, 1-Dimensional engine simulator was used to model the internal combustion engine where the different blend ratios of propane and butane (P100, P90B10, P80B20, P70B30, P60B40 and P50B50) were tested and compared with a gasoline engine operating under same conditions. From the simulation results for the different LPG blends, there was no significant difference in the engine performance and emissions, but when compared with pure gasoline, it was observed that the LPG showed improved engine performance and lower emissions. The engine power output in using the blends was 25% higher compared to using gasoline;CO emission was 50% less, UHC was 20% less while NOx at low speed was significantly lower.

Application of 2-D and 3-D Geo-electrical Resistivity Tomography and Geotechnical Soil Evaluation for Engineering Site Investigation:A Case Study of Okerenkoko Primary School,Warri-Southwest,Delta State,Nigeria

In the design of building structures,joint efforts must be decided to resolve the depth of competent layers across the intended site to safeguard the durability of civil engineering structures and to avert the disastrous consequences of structural failure and collapse.In this study,an integrated methodology that employed DC resistivity tomography involving 2-D and 3-D techniques and geotechnical-soil analysis was used to evaluate subsoil conditions for engineering site investigation at Okerenkoko primary school,in the Warri-southwest area of Delta State,to adduce the phenomena responsible for the visible cracks/structural failure observed in the buildings.The results obtained brought to light the geological structure beneath the subsurface,which consists of four geoelectric layers identified as topsoil,dry/lithified upper sandy layer,wet sand(water-saturated)and peat/clay/sandy clayey soil(highly water-saturated).The deeply-seated peat/clay materials(ρ≤20Ωm)were delineated in the study area to the depths of 17.1 m and 19.8 m from 2-D and 3-D tomography respectively.3-D images presented as horizontal depth slices revealed the dominance of very low resistivity materials i.e.peat/clay/sandy clay within the fourth,fifth and sixth layers at depths ranging from 8.68-12.5 m,12.5-16.9 m and 16.9-21.9 m respectively.The dominance of mechanically unstable peat/clay/sandy clay layers beneath the subsurface,which are highly mobile in response to volumetric changes,is responsible for the noticeable cracks/failure detected on structures within the study site.These observations were validated by a geotechnical test of soil samples in the study area.Atterberg’s limits of the samples revealed plasticity indices of zero.Thus,the soil samples within the depth analyzed were representatives of sandy soil that does not possess any plasticity.The methods justifiably provided relevant information on the subsurface geology beneath the study site and should be appropriated as major tools for engineering site assessment/geotechnical projects.

Prediction models of burst strength degradation for casing with considerations of both wear and corrosion

Casing wear and casing corrosion are serious problems affecting casing integrity failure in deep and ultra-deep wells.This paper aims to predict the casing burst strength with considerations of both wear and corrosion.Firstly,the crescent wear shape is simplified into three categories according to common mathematical models.Then,based on the mechano-electrochemical(M-E)interaction,the prediction model of corrosion depth is built with worn depth as the initial condition,and the prediction models of burst strength of the worn casing and corroded casing are obtained.Secondly,the accuracy of different prediction models is validated by numerical simulation,and the main influence factors on casing strength are obtained.At last,the theoretical models are applied to an ultra-deep well in Northwest China,and the dangerous well sections caused by wear and corrosion are predicted,and the corrosion rate threshold to ensure the safety of casing is obtained.The results show that the existence of wear defects results in a stress concentration and enhanced M-E interaction on corrosion depth growth.The accuracy of different mathematical models is different:the slot ring model is most accurate for predicting corrosion depth,and the eccentric model is most accurate for predicting the burst strength of corroded casing.The burst strength of the casing will be overestimated by more than one-third if the M-E interaction is neglected,so the coupling effect of wear and corrosion should be sufficiently considered in casing integrity evaluation.

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

Mechanical Modeling and Analysis of Stability Deterioration of Production Well During Marine Hydrate Depressurization Production

Different from oil and gas production,hydrate reservoirs are shallow and unconsolidated,whose mechanical properties deteriorate with hydrate decomposition.Therefore,the formations will undergo significant subsidence during depressurization,which will destroy the original force state of the production well.However,existing research on the stability of oil and gas production wells assumes the formation to be stable,and lacks consideration of the force exerted on the hydrate production well by formation subsidence caused by hydrate decomposition during production.To fill this gap,this paper proposes an analytical method for the dynamic evolution of the stability of hydrate production well considering the effects of hydrate decomposition.Based on the mechanical model of the production well,the basis for stability analysis has been proposed.A multi-field coupling model of the force state of the production well considering the effect of hydrate decomposition and formation subsidence is established,and a solver is developed.The analytical approach is verified by its good agreement with the results from the numerical method.A case study found that the decomposition of hydrate will increase the pulling-down force and reduce the supporting force,which is the main reason for the stability deterioration.The higher the initial hydrate saturation,the larger the reservoir thickness,and the lower the production pressure,the worse the stability or even instability.This work can provide a theoretical reference for the stability maintaining of the production well.

Development and technology status of energy storage in depleted gas reservoirs

Utilizing energy storage in depleted oil and gas reservoirs can improve productivity while reducing power costs and is one of the best ways to achieve synergistic development of"Carbon Peak–Carbon Neutral"and"Underground Resource Utiliza-tion".Starting from the development of Compressed Air Energy Storage(CAES)technology,the site selection of CAES in depleted gas and oil reservoirs,the evolution mechanism of reservoir dynamic sealing,and the high-flow CAES and injection technology are summarized.It focuses on analyzing the characteristics,key equipment,reservoir construction,application scenarios and cost analysis of CAES projects,and sorting out the technical key points and existing difficulties.The devel-opment trend of CAES technology is proposed,and the future development path is scrutinized to provide reference for the research of CAES projects in depleted oil and gas reservoirs.

A semi-analytical model for coupled flow in stress-sensitive multi-scale shale reservoirs with fractal characteristics

A large number of nanopores and complex fracture structures in shale reservoirs results in multi-scale flow of oil. With the development of shale oil reservoirs, the permeability of multi-scale media undergoes changes due to stress sensitivity, which plays a crucial role in controlling pressure propagation and oil flow. This paper proposes a multi-scale coupled flow mathematical model of matrix nanopores, induced fractures, and hydraulic fractures. In this model, the micro-scale effects of shale oil flow in fractal nanopores, fractal induced fracture network, and stress sensitivity of multi-scale media are considered. We solved the model iteratively using Pedrosa transform, semi-analytic Segmented Bessel function, Laplace transform. The results of this model exhibit good agreement with the numerical solution and field production data, confirming the high accuracy of the model. As well, the influence of stress sensitivity on permeability, pressure and production is analyzed. It is shown that the permeability and production decrease significantly when induced fractures are weakly supported. Closed induced fractures can inhibit interporosity flow in the stimulated reservoir volume (SRV). It has been shown in sensitivity analysis that hydraulic fractures are beneficial to early production, and induced fractures in SRV are beneficial to middle production. The model can characterize multi-scale flow characteristics of shale oil, providing theoretical guidance for rapid productivity evaluation.

Synergistic anionic/zwitterionic mixed surfactant system with high emulsification efficiency for enhanced oil recovery in low permeability reservoirs

Emulsification is one of the important mechanisms of surfactant flooding. To improve oil recovery for low permeability reservoirs, a highly efficient emulsification oil flooding system consisting of anionic surfactant sodium alkyl glucosyl hydroxypropyl sulfonate(APGSHS) and zwitterionic surfactant octadecyl betaine(BS-18) is proposed. The performance of APGSHS/BS-18 mixed surfactant system was evaluated in terms of interfacial tension, emulsification capability, emulsion size and distribution, wettability alteration, temperature-resistance and salt-resistance. The emulsification speed was used to evaluate the emulsification ability of surfactant systems, and the results show that mixed surfactant systems can completely emulsify the crude oil into emulsions droplets even under low energy conditions. Meanwhile,the system exhibits good temperature and salt resistance. Finally, the best oil recovery of 25.45% is achieved for low permeability core by the mixed surfactant system with a total concentration of 0.3 wt%while the molar ratio of APGSHS:BS-18 is 4:6. The current study indicates that the anionic/zwitterionic mixed surfactant system can improve the oil flooding efficiency and is potential candidate for application in low permeability reservoirs.

A gated recurrent unit model to predict Poisson’s ratio using deep learning

Static Poisson’s ratio(vs)is crucial for determining geomechanical properties in petroleum applications,namely sand production.Some models have been used to predict vs;however,the published models were limited to specific data ranges with an average absolute percentage relative error(AAPRE)of more than 10%.The published gated recurrent unit(GRU)models do not consider trend analysis to show physical behaviors.In this study,we aim to develop a GRU model using trend analysis and three inputs for predicting n s based on a broad range of data,n s(value of 0.1627-0.4492),bulk formation density(RHOB)(0.315-2.994 g/mL),compressional time(DTc)(44.43-186.9 μs/ft),and shear time(DTs)(72.9-341.2μ s/ft).The GRU model was evaluated using different approaches,including statistical error an-alyses.The GRU model showed the proper trends,and the model data ranges were wider than previous ones.The GRU model has the largest correlation coefficient(R)of 0.967 and the lowest AAPRE,average percent relative error(APRE),root mean square error(RMSE),and standard deviation(SD)of 3.228%,1.054%,4.389,and 0.013,respectively,compared to other models.The GRU model has a high accuracy for the different datasets:training,validation,testing,and the whole datasets with R and AAPRE values were 0.981 and 2.601%,0.966 and 3.274%,0.967 and 3.228%,and 0.977 and 2.861%,respectively.The group error analyses of all inputs show that the GRU model has less than 5% AAPRE for all input ranges,which is superior to other models that have different AAPRE values of more than 10% at various ranges of inputs.

Flow Regimes in Bubble Columns with and without Internals: A Review

Hydrodynamics characterization in terms offlow regime behavior is a crucial task to enhance the design of bubble column reactors and scaling up related methodologies.This review presents recent studies on the typicalflow regimes established in bubble columns.Some effort is also provided to introduce relevant definitions pertaining to thisfield,namely,that of“void fraction”and related(local,chordal,cross-sectional and volumetric)variants.Experimental studies involving different parameters that affect design and operating conditions are also discussed in detail.In the second part of the review,the attention is shifted to cases with internals of various types(perfo-rated plates,baffles,vibrating helical springs,mixers,and heat exchanger tubes)immersed in the bubble columns.It is shown that the presence of these elements has a limited influence on the global column hydrodynamics.However,they can make the homogeneousflow regime more stable in terms of transition gas velocity and transi-tion holdup value.The last section is used to highlight gaps which have not beenfilled yet and future directions of investigation.

Experimental investigation into effects of the natural polymer and nanoclay particles on the EOR performance of chemical flooding in carbonate reservoirs

This paper aims to investigate the tragacanth gum potential as a natural polymer combined with natural clay mineral(montmorillonite,kaolinite,and illite)nanoparticles(NPs)to form NP-polymer suspension for enhanced oil recovery(EOR)in carbonate reservoirs.Thermal gravimetric analysis(TGA)tests were conducted initially in order to evaluate the properties of tragacanth gum.Subsequently,scanning electron microscopy(SEM)and energy-dispersive X-ray(EDX)tests were used to detect the structure of clay particles.In various scenarios,the effects of natural NPs and polymer on the wettability alteration,interfacial tension(IFT)reduction,viscosity improvement,and oil recovery were investigated through contact angle system,ring method,Anton Paar viscometer,and core flooding tests,respectively.The entire experiment was conducted at 25,50,and 75℃,respectively.According to the experimental results,the clay minerals alone did not have a significant effect on viscosity,but the addition of minerals to the polymer solution leads to the viscosity enhancement remarkably,resulting mobility ratio improvement.Among clay NPs,the combination of natural polymer and kaolinite results in increased viscosity at all temperatures.Considerable wettability alteration was also observed in the case of natural polymer and illite NPs.Illite in combination with natural polymer showed an ability in reducing IFT.Finally,the results of displacement experiments revealed that the combination of natural polymer and kaolinite could be the best option for EOR due to its substantial ability to improve the recovery factor.