A review of reservoir damage during hydraulic fracturing of deep and ultra-deep reservoirs

Deep and ultra-deep reservoirs have gradually become the primary focus of hydrocarbon exploration as a result of a series of significant discoveries in deep hydrocarbon exploration worldwide.These reservoirs present unique challenges due to their deep burial depth(4500-8882 m),low matrix permeability,complex crustal stress conditions,high temperature and pressure(HTHP,150-200℃,105-155 MPa),coupled with high salinity of formation water.Consequently,the costs associated with their exploitation and development are exceptionally high.In deep and ultra-deep reservoirs,hydraulic fracturing is commonly used to achieve high and stable production.During hydraulic fracturing,a substantial volume of fluid is injected into the reservoir.However,statistical analysis reveals that the flowback rate is typically less than 30%,leaving the majority of the fluid trapped within the reservoir.Therefore,hydraulic fracturing in deep reservoirs not only enhances the reservoir permeability by creating artificial fractures but also damages reservoirs due to the fracturing fluids involved.The challenging“three-high”environment of a deep reservoir,characterized by high temperature,high pressure,and high salinity,exacerbates conventional forms of damage,including water sensitivity,retention of fracturing fluids,rock creep,and proppant breakage.In addition,specific damage mechanisms come into play,such as fracturing fluid decomposition at elevated temperatures and proppant diagenetic reactions at HTHP conditions.Presently,the foremost concern in deep oil and gas development lies in effectively assessing the damage inflicted on these reservoirs by hydraulic fracturing,comprehending the underlying mechanisms,and selecting appropriate solutions.It's noteworthy that the majority of existing studies on reservoir damage primarily focus on conventional reservoirs,with limited attention given to deep reservoirs and a lack of systematic summaries.In light of this,our approach entails initially summarizing the current knowledge pertaining to the types of fracturing fluids employed in deep and ultra-deep reservoirs.Subsequently,we delve into a systematic examination of the damage processes and mechanisms caused by fracturing fluids within the context of hydraulic fracturing in deep reservoirs,taking into account the unique reservoir characteristics of high temperature,high pressure,and high in-situ stress.In addition,we provide an overview of research progress related to high-temperature deep reservoir fracturing fluid and the damage of aqueous fracturing fluids to rock matrix,both artificial and natural fractures,and sand-packed fractures.We conclude by offering a summary of current research advancements and future directions,which hold significant potential for facilitating the efficient development of deep oil and gas reservoirs while effectively mitigating reservoir damage.

Pre-Drilling Prediction Techniques on the High-Temperature High-Pressure Hydrocarbon Reservoirs Offshore Hainan Island,China

Decreasing the risks and geohazards associated with drilling engineering in high-temperature high-pressure(HTHP) geologic settings begins with the implementation of pre-drilling prediction techniques(PPTs). To improve the accuracy of geopressure prediction in HTHP hydrocarbon reservoirs offshore Hainan Island, we made a comprehensive summary of current PPTs to identify existing problems and challenges by analyzing the global distribution of HTHP hydrocarbon reservoirs, the research status of PPTs, and the geologic setting and its HTHP formation mechanism. Our research results indicate that the HTHP formation mechanism in the study area is caused by multiple factors, including rapid loading, diapir intrusions, hydrocarbon generation, and the thermal expansion of pore fluids. Due to this multi-factor interaction, a cloud of HTHP hydrocarbon reservoirs has developed in the Ying-Qiong Basin, but only traditional PPTs have been implemented, based on the assumption of conditions that do not conform to the actual geologic environment, e.g., Bellotti's law and Eaton's law. In this paper, we focus on these issues, identify some challenges and solutions, and call for further PPT research to address the drawbacks of previous works and meet the challenges associated with the deepwater technology gap. In this way, we hope to contribute to the improved accuracy of geopressure prediction prior to drilling and provide support for future HTHP drilling offshore Hainan Island.

Research on thermal insulation materials properties under HTHP conditions for deep oil and gas reservoir rock ITP-Coring

Deep oil and gas reservoirs are under high-temperature conditions,but traditional coring methods do not consider temperature-preserved measures and ignore the influence of temperature on rock porosity and permeability,resulting in distorted resource assessments.The development of in situ temperaturepreserved coring(ITP-Coring)technology for deep reservoir rock is urgent,and thermal insulation materials are key.Therefore,hollow glass microsphere/epoxy resin thermal insulation materials(HGM/EP materials)were proposed as thermal insulation materials.The materials properties under coupled hightemperature and high-pressure(HTHP)conditions were tested.The results indicated that high pressures led to HGM destruction and that the materials water absorption significantly increased;additionally,increasing temperature accelerated the process.High temperatures directly caused the thermal conductivity of the materials to increase;additionally,the thermal conduction and convection of water caused by high pressures led to an exponential increase in the thermal conductivity.High temperatures weakened the matrix,and high pressures destroyed the HGM,which resulted in a decrease in the tensile mechanical properties of the materials.The materials entered the high elastic state at 150℃,and the mechanical properties were weakened more obviously,while the pressure led to a significant effect when the water absorption was above 10%.Meanwhile,the tensile strength/strain were 13.62 MPa/1.3%and 6.09 MPa/0.86%at 100℃ and 100 MPa,respectively,which meet the application requirements of the self-designed coring device.Finally,K46-f40 and K46-f50 HGM/EP materials were proven to be suitable for ITP-Coring under coupled conditions below 100℃ and 100 MPa.To further improve the materials properties,the interface layer and EP matrix should be optimized.The results can provide references for the optimization and engineering application of materials and thus technical support for deep oil and gas resource development.

Subsurface analytics: Contribution of artificial intelligence and machine learning to reservoir engineering, reservoir modeling, and reservoir management

Traditional Numerical Reservoir Simulation has been contributing to the oil and gas industry for decades.The current state of this technology is the result of decades of research and development by a large number of engineers and scientists.Starting in the late 1960s and early 1970s,advances in computer hardware along with development and adaptation of clever algorithms resulted in a paradigm shift in reservoir studies moving them from simplified analogs and analytical solution methods to more mathematically robust computational and numerical solution models.

Productivity Analysis Method of Abnormal High-Pressure Gas Reservoir in Ying-Qiong Basin

Ying-Qiong Basin in the west of South China Sea contains plenty of abnormal high-pressure gas reservoirs, whose stress sensitivity is crucial for well productivity. To explore the influence of stress sensitivity on production, the variable outlet back pressure stress sensitivity experiments were applied to test core sample permeability under different burden pressure and obtain the relational expression of power function of core stress sensitivity. Afterwards, new productivity equation is deduced in consideration of reservoir stress sensitivity, and the affection of stress sensitivity on production is analyzed. The result demonstrates close link between stress sensitivity and productivity, since single well production decreases dramatically when reservoir stress sensitivity has been taken into account. This research is constructive for well-testing data interpretation in stress sensitive gas reservoirs.

Artificial enhancement of sturgeon stock in freshwater reservoirs:A case study on sterlet Acipenser ruthenus of the Kama reservoir

The paper presents the results of the sterlet sturgeon Acipenser ruthenus reintroduction into the Kama Reservoir(Perm Krai,Russia)by sterlet fry from hatcheries into the reservoir since 2001.The effectiveness of hatchery releases was evaluated by monitoring gillnet sampling 2014-2020.The results showed that the species was reported throughout entire Kama Reservoir,which contrasts with 1988-1994 data,when the species was registered only at the upper part of the reservoir near the Kama River mouth.The highest number and biomass values of catch per unit effort(CPUE)of the sterlet were found in submerged river channels-the deepest habitats with flow conditions which are close to the ecological requirements of the species.The distribution of the sterlet within the channel biotopes of the reservoir is defined by reduced variability.The biggest individuals and the widest size range of the sterlet were typical for the upper area of the reservoir and Chusovskoy Bay,which have the greatest flow.The smallest sizes were typical for fish from catches near the dam of the reservoir,which is consistent with the published data on the distribution of sterlet in reservoir conditions.The presented results may indicate the artificial formation of the sterlet stock in the Kama Reservoir maintained by hatchery releases.The revealed distribution of the sterlet in the reservoir determines the direction of further research on natural reproduction and thus formation of an artificially formed population.Also,future studies are required to assess the possibility of hybridization of the sterlet of artificial origin with natural populations of the species,since the low genetic diversity of the sterlet released into the Kama Reservoir which can lead to irreversible loss of genetic individuality of the natural stocks of the species in the catchment of upper course of the Kama River.

High-pressure capacity expansion and water injection mechanism and indicator curve model for fractured-vuggy carbonate reservoirs

Water injection for oil displacement is one of the most effective ways to develop fractured-vuggy carbonate reservoirs.With the increase in the number of rounds of water injection,the development effect gradually fails.The emergence of high-pressure capacity expansion and water injection technology allows increased production from old wells.Although high-pressure capacity expansion and water injection technology has been implemented in practice for nearly 10 years in fractured-vuggy reservoirs,its mechanism remains unclear,and the water injection curve is not apparent.In the past,evaluating its effect could only be done by measuring the injection-production volume.In this study,we analyze the mechanism of high-pressure capacity expansion and water injection.We propose a fluid exchange index for high-pressure capacity expansion and water injection and establish a discrete model suitable for high-pressure capacity expansion and water injection curves in fractured-vuggy reservoirs.We propose the following mechanisms:replenishing energy,increasing energy,replacing energy,and releasing energy.The above mechanisms can be identified by the high-pressure capacity expansion and water injection curve of the well HA6X in the Halahatang Oilfield in the Tarim Basin.By solving the basic model,the relative errors of Reservoirs I and II are found to be 1.9%and 1.5%,respectively,and the application of field examples demonstrates that our proposed high-pressure capacity expansion and water injection indicator curve is reasonable and reliable.This research can provide theoretical support for high-pressure capacity expansion and water injection technology in fracture-vuggy carbonate reservoirs.

Rock thin-section analysis and identification based on artificial intelligent technique

Rock thin-section identification is an indispensable geological exploration tool for understanding and recognizing the composition of the earth.It is also an important evaluation method for oil and gas exploration and development.It can be used to identify the petrological characteristics of reservoirs,determine the type of diagenesis,and distinguish the characteristics of reservoir space and pore structure.It is necessary to understand the physical properties and sedimentary environment of the reservoir,obtain the relevant parameters of the reservoir,formulate the oil and gas development plan,and reserve calculation.The traditional thin-section identification method has a history of more than one hundred years,which mainly depends on the geological experts'visual observation with the optical microscope,and is bothered by the problems of strong subjectivity,high dependence on experience,heavy workload,long identification cycle,and incapability to achieve complete and accurate quantification.In this paper,the models of particle segmentation,mineralogy identification,and pore type intelligent identification are constructed by using deep learning,computer vision,and other technologies,and the intelligent thinsection identification is realized.This paper overcomes the problem of multi-target recognition in the image sequence,constructs a fine-grained classification network under the multi-mode and multi-light source,and proposes a modeling scheme of data annotation while building models,forming a scientific,quantitative and efficient slice identification method.The experimental results and practical application results show that the thin-section intelligent identification technology proposed in this paper does not only greatly improves the identification efficiency,but also realizes the intuitive,accurate and quantitative identification results,which is a subversive innovation and change to the traditional thin-section identification practice.

Analog Optical Computing for Artificial Intelligence

The rapid development of artificial intelligence(AI)facilitates various applications from all areas but also poses great challenges in its hardware implementation in terms of speed and energy because of the explosive growth of data.Optical computing provides a distinctive perspective to address this bottleneck by harnessing the unique properties of photons including broad bandwidth,low latency,and high energy efficiency.In this review,we introduce the latest developments of optical computing for different AI models,including feedforward neural networks,reservoir computing,and spiking neural networks(SNNs).Recent progress in integrated photonic devices,combined with the rise of AI,provides a great opportunity for the renaissance of optical computing in practical applications.This effort requires multidisciplinary efforts from a broad community.This review provides an overview of the state-of-the-art accomplishments in recent years,discusses the availability of current technologies,and points out various remaining challenges in different aspects to push the frontier.We anticipate that the era of large-scale integrated photonics processors will soon arrive for practical AI applications in the form of hybrid optoelectronic frameworks.

Application and development trend of artificial intelligence in petroleum exploration and development

Aiming at the actual demands of petroleum exploration and development,this paper describes the research progress and application of artificial intelligence(AI)in petroleum exploration and development,and discusses the applications and development directions of AI in the future.Machine learning has been preliminarily applied in lithology identification,logging curve reconstruction,reservoir parameter estimation,and other logging processing and interpretation,exhibiting great potential.Computer vision is effective in picking of seismic first breaks,fault identification,and other seismic processing and interpretation.Deep learning and optimization technology have been applied to reservoir engineering,and realized the real-time optimization of waterflooding development and prediction of oil and gas production.The application of data mining in drilling,completion,and surface facility engineering etc.has resulted in intelligent equipment and integrated software.The potential development directions of artificial intelligence in petroleum exploration and development are intelligent production equipment,automatic processing and interpretation,and professional software platform.The highlights of development will be digital basins,fast intelligent imaging logging tools,intelligent seismic nodal acquisition systems,intelligent rotary-steering drilling,intelligent fracturing technology and equipment,real-time monitoring and control of zonal injection and production.

Use of Artificial Intelligence in the Issue of Protection against Negative Impact of Floods

The paper follows possible specification of a control algorithm of a WS （water management system） during floods using the procedures of AI （artificial intelligence）. The issue of minimizing negative impacts of floods represents influencing and controlling a dynamic process of the system where the main regulation elements are water reservoirs. Control of water outflow from reservoirs is implicitly based on the used model （titled BW） based on FR （fuzzy regulation）. Specification of a control algorithm means dealing with the issue of preparing a knowledge base for the process of tuning fuzzy regulators based on an I/O （input/output） matrix obtained by optimization of the target behaviour of WS. Partial results can be compared with the regulation outputs when specialized tuning was used for the fuzzy regulator of the control algorithm. Basic approaches follow from the narrow relation on BW model use to simulate floods, without any connection to real water management system. A generally introduced model allows description of an outflow dynamic system with stochastic inputs using submodels of robust regression in the outflow module. The submodels are constructed on data of historical FS （flood situations）.

页岩气勘探开发实验技术研究进展与发展方向

页岩气开发的成功得益于全新地质理论的建立及工程技术的进步,其中地质理论的创新离不开实验技术的突破与发展,但随着页岩气进入全面开发的新阶段,对实验方法、基础理论等方面提出了更高要求和技术挑战。为此,针对页岩气勘探开发过程中的关键问题,从页岩矿物组成、岩石物性、孔隙结构、含气性及可压性等5个方面展示了地质实验研究的进展和应用实例,分析了页岩气勘探开发实验研究面临的问题和挑战,并展望了其未来的发展方向和趋势。研究结果表明:①薄片鉴定和X射线衍射是页岩矿物分析最有效的定性定量方法,开展矿物成因实验研究对于揭示页岩成储规律及地质工程甜点段优选有着更重要的指导意义;②页岩物性测试方法多且标准不统一,需要根据研究目的选择适用的测试方法,开展地层条件下的物性测试对储量计算及开发效果评价更有帮助;③页岩孔隙结构表征已形成了从宏观到微观、从定性到定量、从二维到三维的综合评价方法,可指导开发层系划分和水平井靶窗优选;④页岩含气性评价已形成了现场含气量测试、等温吸附、气体碳同位素监测等实验方法,揭示了页岩气赋存状态和解吸规律,可有效指导气井生产制度制订和采收率提高;⑤页岩可压性评价已形成了X射线衍射、多尺度的裂缝网络刻画、三轴力学、差应变法、声波各向异性法及声发射法等实验方法,可指导水平井设计和压裂工艺优化。结论认为,建立跨尺度多方法深度融合的实验体系、加强原位条件下的页岩气储层评价、开展关键参数的动态演化和基于大数据模型的人工智能实验评价等是下一步研究方向,持续完善页岩气实验地质技术体系方可为页岩气高效勘探开发提供更好的理论基础和技术支撑。

基于知识驱动图版约束的致密砂岩气储层测井参数智能预测

中国致密砂岩气资源潜力巨大,是天然气增储上产的重要对象,但致密砂岩储层空间类型多样,纵横向变化大,“四性”关系复杂,测井系列多样,测井项目少,常规测井技术评价致密储层参数难度大、效率低。为此,以四川盆地金秋、天府气田致密气为对象,构建构造区块—油气田—油气藏—测井解释图版主线,形成了致密砂岩气储层测井参数解释知识图谱,并通过神经网络算法对样本数据进行处理并约束模型结果,建立了图版约束的人工智能储层测井参数预测模型,实现了专家经验与数据双向驱动的储层测井参数智能预测。研究结果表明:(1)新智能模型融入了专家经验图版信息,且构建了专家经验与数据双向驱动的智能参数预测方法,极大地提升了模型对测井领域知识的理解能力和实践能力;(2)基于常规测井曲线,通过特征处理实现多维特征的挖掘,衍生出新曲线,与常规曲线一起作为输入进行模型强化训练,有助于提高解释模型的准确率;(3)实际应用结果表明,采用知识驱动图版约束的致密砂岩气储层参数智能预测方法计算的孔隙度和渗透率与岩心分析孔隙度及渗透率之间的误差分别为7.9%和15%,计算的含水饱和度与密闭取心饱和度之间的误差仅为5%。结论认为,基于知识驱动图版约束的致密砂岩气储层参数智能预测技术可以解决老井人工评价工作量大,测井解释标准不统一的问题,并可实现快速高效测井智能评价及潜力优选,将有力地推动了人工智能在测井领域的深度应用。

基于MLR–ANN算法的地应力场反演与裂缝预测

中国页岩气储层埋藏深,受构造运动影响,地应力分布规律复杂,传统方法很难准确反演区域地应力大小和方向。提出多元线性回归和人工神经网络的耦合算法,对川南长宁—建武区块的页岩气储层及周边地应力场进行反演,并采用综合破裂系数法,对储层裂缝进行预测,划分裂缝发育区域。研究表明,基于多元回归和神经网络的耦合算法能准确反演区域的地应力场分布规律。研究区的地应力以挤压应力为主,方向在NE115°左右。受构造运动产生的断层周边应力较为集中,易发育剪切裂缝,裂缝以发育和较发育程度为主。研究区在邻近龙马溪组底部的五峰组上段和构造大断层部位裂缝发育程度较高。研究成果对该区块完善页岩气开采的井网布置、压裂优化设计和套管损坏防治等有一定的参考价值。

基于BP神经网络的储层渗透率预测方法研究

传统的间接解释法预测主要采用线性回归方法进行渗透率预测,但此方法存在一个极大的缺点,因为所有数据不一定都是线性关系故采用线性回归方式预测精确度差别较大。针对此问题论文提出基于BP神经网络的储层渗透率预测方法对储层存在的大多数非线性关系数据进行预测。我们首先对选取的原始测井数据进行数据清洗,数据归一化处理;然后,采用BP神经网络算法进行数据特征分析计算,最后使用岩性剖面解释数据验证预测结果。论文采用基于BP神经网络利用测井曲线对储层渗透率预测的方法对松辽盆地构造区某井进行实验,达到了渗透率平均相对预测误差减小、精度大大提高,并且满足测井解释储层参数精度要求。

火山岩气藏未动用储量评价优选及开发技术界限研究

为了合理确定火山岩气藏未动用储量区块开发动用次序,给出相应的开发技术政策等问题,采用层次分析方法对未开发储量区块进行了优选排队,确定了12个典型区块的开发动用次序;采用蒙特卡洛法重新估算了典型区块的地质储量,评价后地质储量分险在23%~35%之间,储量分险较高;结合人工智能辅助评价技术对典型区块开展了敏感性分析、自动历史拟合、最优化方法以及不确定分析等研究,给出一种新的开发指标预测方法和界限研究;应用盈亏平衡原理,给出了火山岩气藏未开发储量区块的动用技术界限,单井初期经济极限产量1.92×10^(4) m^(3)/d,经济极限井控地质储量1.57×10^(8) m^(3),经济极限有效厚度11.05 m。为类似火山岩气藏未开发储量评价与开发提供参考依据。

复杂油气藏储层预测方法综述

随着油气勘探不断深入,传统储层逐渐减少,使得岩溶储层、裂缝储层、页岩储层、煤层气等复杂储层成为重要勘探目标。这些复杂储层往往具有多种非均质性特征,包括岩性变化、孔隙裂缝、多介质分布等,导致储层的空间分布和特性变化极为复杂,深入研究复杂储层预测方法具有重要的理论和应用价值。本文重点分析了岩溶储层、裂缝储层、页岩储层和煤层气储层等复杂油气藏的形成机理和勘探难点;系统总结了相关研究的发展情况和应用方法,为相关领域研究人员提供了有益的参考。指出未来复杂储层预测的发展将集中在多源信息融合、数据集成、高分辨率地震技术和人工智能等方向,这些技术的应用有望提高预测精度和效率,为复杂气藏的勘探和开发提供更为全面和有效的支持。

Test and Study of the Rock Pressure Sensitivity for KeLa-2 Gas Reservoir in the Tarim Basin

KeLa-2 gas reservoir is the largest uncompartimentalized gas field so far discovered in China, with a reserve of hundreds of billions of cubic meters of dry gas. It has such features as extremely long interval (550m), high pressure (74.5MPa) and pressure coefficient (2.022). Gas reservoirs with a pressure coefficient of over 2.0 are not commonly found. The abnormal high-pressure reservoirs are quite different in characteristic and performance during the process of depletion exploitation. Therefore, it is necessary to know the property of pressure sensitivity for this abnormal high-pressure reservoir. The aim of this paper is to test the reservoir pressure sensitivity and to analyze its effect on the deliverability of gas. Through some experiments, the permeability change with the confining pressure of rock samples from KeLa-2 abnormal high-pressure gas reservoir is measured. A power function is used to match the measured data, and to derive an empirical equation to describe the change of permeability through the change of the reservoir pressure or effective overburden pressure. Considering the permeability change during the development of reservoirs, a conventional deliverability equation is modified, and the deliverability curve for KeLa-2 gas reservoir is predicted. The research indicates that the extent of the pressure sensitivity of rock samples from KeLa-2 is higher than that from the Daqing oilfield. KeLa-2 reservoir rock has the feature of an undercompaction state. The pressure sensitivity of a reservoir may decrease the well deliverability. It is concluded that for KeLa-2 reservoir the predicted absolute open flow (AOF), when the pressure sensitivity is taken into account, is approximately 70% of the AOF when permeability is constant and does not change with pressure.

人工强制混合藻华控制研究进展:理论、技术与机制

人工强制混合技术通过调控水动力条件对藻类生长和结构产生影响,已在全球湖库应用多年,国内外相关研究较多.然而,缺少综述性整理,尤其是对不同技术应用前提、适用条件及优缺点等认识不够,导致技术错用而没有发挥其优势,出现了控藻效果差,甚至无效等现象.本文主要结论如下:①该类技术通过调控混合层深度(Z_(mix))控制藻类生物量与结构变化,其应用效果主要与湖库水深、混合器平面布设、系统运行方式和混合效率有关;②该类技术控藻周期(藻密度削减95%以上)约为2周,控藻推荐的Z_(mix)阈值>15m;③该类技术主要适用于深水型水体,遴选控藻技术时应考虑适用条件、成本效益等.