Reservoir Description Research Articles

Energy method of geophysical logging lithology based on K-means dynamic clustering analysis

Lithology identification is an important part of reservoir evaluation and reservoir description when processing and interpreting geophysical record data. Clustering analysis refers to the analysis process of grouping a collection of physical or abstract objects into several classes composed of similar objects. K-means clustering algorithm is an iterative clustering analysis algorithm. In this paper, seven mechanical property parameters of 49 rock samples are selected as experimental data in an engineering survey, and the geophysical logging method of K-means dynamic clustering analysis is adopted. The rock samples are divided into three categories, and the classification results are matched by mechanical property parameter method. By changing the order of data grouping, the misjudgment rates were 0.021, 0.021 and 0.102, respectively. Therefore, it is feasible and effective to use k-means dynamic clustering analysis to classify lithology. The number of samples decreased to 15, and the misjudgment rate increased to 0.267 The results of K-means dynamic clustering analysis may be different from the actual situation of rock sample data selection.

Research on Reservoir Numerical Simulation with Consideration of Time-dependent Physical Properties

Fine reservoir description is an important prerequisite to ensure the scientific and efficient development of oilfields. Reservoir numerical simulation is an important tool for efficient development. The accuracy of reservoir description determines the reliability of numerical simulation results. After long-term water injection development, the physical properties of the reservoir have undergone great changes. How to identify the time-dependent law of reservoir physical properties and finely characterize them during numerical simulation has been the focus and research difficulty in this field in recent years. This paper combines the results of previous studies on the changes of reservoir physical properties in the later stage of water drive development in Area A with numerical simulation methods. Predecessors realized time-dependent numerical simulation methods include staged numerical simulation and self-developed numerical simulation programs. Although the above two types of methods can achieve the purpose of realizing time-dependent numerical simulation, they all have certain defects. The segmented simulation method requires multiple modeling, heavy workload, multiple manual operations, and poor continuity and non-directional calculation results. Although the self-developed numerical simulation program calculation results can have better continuity and directionality, but this requires a lot of manpower and material resources to carry out the research. The research and development cost is high, the cycle is long, and the simulation accuracy and speed are difficult to reach the level of commercial numerical simulation software in the short term. Based on the above analysis, this paper has developed a simulation program TD-SIM (Time Dependent Simulation) that combines the commercial numerical simulation software ECLIPSE and can realize the time-dependent function of the reservoir permeability field. This program has convenient use and low cost. At the same time, it is necessary to take into account faster simulation speed, higher simulation accuracy, and better continuity and directionality of the simulation results.

Reservoir characterization and production history matching of Lower Cretaceous, Muddy Formation in Ranch Creek area, Bell Creek oil field, Southeastern Montana, USA

A detailed reservoir description of the Lower Cretaceous, Muddy Formation is an essential step for developing an enhanced oil recovery project in the Bell Creek field. The applied workflow highlighted the role of key uncertainties that were not well addressed during early field development. Several challenges including: stratigraphic subdivision of the reservoir units, rock type complexity due to diagenesis, petrophysical data inconsistencies and fluid contacts have historically led to hurdles in reservoir development. The study outlines a workflow used to refine some of these uncertainties and generate better inputs for the geologic model. The Ranch Creek area at Bell Creek field in southeastern Montana is characterized by a clastic sequence of mixed rock types from argillaceous mudstone, bioturbated tightly cemented to clean sandstone lithofacies. Delta front to fluvial-dominated depositional settings as well as diagenesis played an important role in controlling reservoir quality and geometry. The facies complexity is reflected in rock properties controlling fluid migration through the reservoir intervals. The lithofacies types are clustered into three groups, each with characteristic porosity and permeability cutoffs based on core plugs measurements. The rock types are linked to depositional facies obtained from the petrographic description and calibrated to the clusters derived from the capillary pressure measurements. Porosity distribution within the reservoir is biased to the diagenetic complexity and rock type distribution. Three different transforms were used to calculate the corresponding permeability to the modeled porosity parameter. Modeling water saturation for the three different capillary pressure groups using the J-function approach improves the spatial distribution of water saturation and leads to a better match of historical oil and water production rates.Reservoir simulation is crucial to improve our understanding of rock and fluid properties. Integrating dynamic data such as pressure, production and injection rates helps to reduce the uncertainty of the model parameters. The history match process highlights the need to tune the output of the static model. Iterations on the static model, focused on changing the ranges of key parameters such as facies volume fractions and water saturation that contain the most uncertainties. Applying these changes results in an improved geological model that provides a better data to the simulation model. The tuned static model was able to reproduce the observed results. Matching the pressure data supports a valid oil-in-place which is consistence with historical volumetric calculation.

On a Deep Learning Method of Estimating Reservoir Porosity

Porosity is an important parameter for the oil and gas storage, which reflects the geological characteristics of different historical periods. The logging parameters obtained from deep to shallow strata show the stratigraphic sedimentary characteristics in different geological periods, so there is a strong nonlinear mapping relationship between porosity and logging parameters. It is very important to make full use of logging parameters to predict the shale content and porosity of the reservoir for precise reservoir description. Deep neural network technology has strong data structure mining ability and has been applied to shale content prediction in recent years. In fact, the gated recurrent unit (GRU) neural network has further advantage in processing serialized data. Therefore, this study proposes a method to predict porosity by combining multiple logging parameters based on the GRU neural network. Firstly, the correlation measurement method based on Copula function is used to select the logging parameters most relevant to porosity parameters. Then, the GRU neural network is used to identify the nonlinear mapping relationship between logging data and porosity parameters. The application results in an exploration area of the Ordos basin show that this method is superior to multiple regression analysis and recurrent neural network method, which indicates that the GRU neural network is more effective in predicting a series of reservoir parameters such as porosity.

Description and Understanding of Complex Fault Block Reservoir Geology

Reservoir description is an important step in oilfield development. Before mining, using various technical means to analyze and study oilfield structure can achieve the purpose of fully understanding the reservoir and provide theoretical basis for reservoir development. In this exploration, the background of description of complex fault block reservoir geology is analyzed, and X35 reservoir is selected as the research object to study the regional geological reservoir. By analyzing the fault distribution characteristics of X35 reservoir, the reservoir strata are divided. The three-dimensional (3D) geological model of the reservoir is established by using the method of facies-controlled modeling and numerical simulation. Finally, the 3D geological model is tested. The results show that the model has high accuracy and reflects the distribution of X35 reservoir, which has a certain guiding significance for the exploration and effective development of the remaining oil in the reservoirs distributed in this area.

Oil-gas-water system study in fine reservoir description researches—taking Yulou oil-bearing sets in the West depression in Liaohe Basin in China as an example

Taking a steam flooding experimental area in the West depression in Liaohe Basin in China as an example, the development characteristics of reservoir oil-gas-water system is studied using methods of geological statistics and comparative analysis based on the comprehensive of core, well logging, analytical test, and development performance data. The aimed stratum has good oil-bearing properties, and the oil content level of cores is mainly oil sand and oil-bearing sandstone. The lower limit of effective reservoir has a porosity of 28% and a permeability of 30 mD. In the aimed stratum, the oil viscosity increases gradually with the increase of oil density, and the correlation between the wax content and density of oil is not obvious. With the increase of oil viscosity, the resin and asphaltene contents increase. And with the increase of oil density, the resin and asphaltene contents gradually increase as well. The oil density is relatively big, and most of the 14 samples have a density greater than 0.97 g/cm3. The oil viscosity is mostly greater than 5000 mpa s. The wax content is relatively low (mostly less than 2.5%) with the maximum of 4%. The contents of resin and asphaltene are both greater than 38%, which are relatively high. The formation water is NaHCO3 type which is slightly alkaline. The oil-water contacts of different reservoirs in Yulou oil-bearing sets I are between depth of 1020 m and 1060 m. The distribution of oil layers is jointly controlled by the sediments and structures factors. For the thickness of single oil layer, the maximum is more than 5 m, while the minimum is only one to 2 m. The edge and bottom water plays a very important role in the development of oilfields. In designing and executing various EOR methods in the process of thermal recovery, the influence of reservoir oil-gas-water system should be fully taken into account.

Full-waveform inversion of intensity-focused seismic data

Full-waveform inversion (FWI) is a promising tool for the comprehensive analysis of seismic data because it involves modeling the complete elastodynamic phenomena in 3D for the estimation of medium parameters. However, the application for reservoir characterization is still limited in achieving the inverse problem solution using reasonable computational resources and the required model resolution. Following a data-driven approach, we have implemented the isotropic elastic FWI with sensitivity emphasis on a prescribed target zone of the propagation volume, showing that the method is useful in improving the estimation over the target zone compared with nonlocalized methods. Our method uses the superposition principle to construct multipoint sources that focus the primary wave intensity (seismic illumination) over the specific target area. Thus, the secondary wavefield scattered from the target area to the receivers increases, providing improved information on the model parameters that control the property heterogeneity within the target zone. We design two types of focused macrosource (FMS) configurations that accomplish this objective: the convergent FMS and the combined beam FMS. Although multipoint seismic sources are not implemented in the field for practical reasons, they can be synthetized computationally using the superposition principle, interpolation, and the current seismic survey data (point-source) to compose the FMS observed gathers, which are then used as input data for the FWI. We perform elastic FWI synthetic tests in a 2D modified Marmousi model to compare the inversion performance with focused and nonfocused data, under the same computational conditions, with and without the presence of noise. Our tests show faster convergence and improved estimation in the target zone with the use of the intensity-focused seismic data for FWI; improved focusing effects are expected in 3D. Being target-oriented, the method is suitable for reducing computational requirements in elastic FWI application for reservoir description.

Artificial Neural Network Models and Predicts Reservoir Parameters

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 19854, “Modeling and Prediction of Resistivity, Capillary Pressure, and Relative Permeability Using Artificial Neural Network,” by Mustafa Ba alawi, SPE, King Fahd University of Petroleum and Minerals; Salem Gharbi, SPE, Saudi Aramco; and Mohamed Mahmoud, King Fahd University of Petroleum and Minerals, prepared for the 2020 International Petroleum Technology Conference, Dhahran, Saudi Arabia, 13–15 January. The paper has not been peer reviewed. Copyright 2020 International Petroleum Technology Conference. Reproduced by permission. Capillary pressure and relative permeability are essential measurements that affect multiphase fluid flow in porous media directly. The processes of measuring these parameters, however, are both time-consuming and expensive. Artificial-intelligence methods have achieved promising results in modeling extremely complicated phenomena in the industry. In the complete paper, the authors generate a model by using an artificial-neural-network (ANN) technique to predict both capillary pressure and relative permeability from resistivity. Capillary Pressure and Resistivity Capillary pressure and resistivity are two of the most significant parameters governing fluid flow in oil and gas reservoirs. Capillary pressure, the pressure difference over the interface of two different immiscible fluids, traditionally is measured in the laboratory. The difficulty of its calculation is related to the challenges of maintaining reservoir conditions and the complexity of dealing with low-permeability and strong heterogeneous samples. Moreover, unless the core materials are both available and representative, a restricted number of core plugs will lead to inadequate reservoir description. On the other hand, resistivity can be obtained by either lab-oratory analysis or through typical and routine well-logging tools in real time. Both parameters have similar attributes, given their dependence on wetting-phase saturation. Despite many studies in the literature that are reviewed in the complete paper, improvement of capillary pressure and resistivity modeling remains an open research area. Artificial Intelligence in Petroleum Engineering In addition to labor and expense concerns, conventional methods to measure resistivity, capillary pressure, and relative permeability depend primarily, with the exception of resistivity from well logs, on the availability of core samples of the desired reservoir. The literature provides several attempts to model these parameters in order to avoid many of the requirements of measurement. However, the performance of many of these models is restricted by assumptions and constraints that require further processing. For example, the accuracy of prediction of capillary pressure from resistivity is highly dependent on the tested core permeability, which requires measuring it as well to achieve the full potentiality of the model.

ANALYSIS OF RECOMMENDED MEASURES AIMED AT OPTIMISING AND IMPROVING THE DEVELOPMENT PROCESS AT THE PRORVA OIL DEPOSIT

Determination of the optimal parameters of deposit development systems depending on the features of the geological structure and operating modes of the deposits, the main provisions of the regulation of control and regulation of the development process are considered as factors affecting the optimisation of the level of oil production. To increase the efficiency of deposit development and justify measures to control and regulate the development process, the design technological indicators are specified in this paper. The analysis determines the current state of development of the Zapadnaya Prorva deposit and its recoverable oil reservoirs, an assessment of the effectiveness of the implementation of previously approved design decisions, factoring in the existing understanding of the geological structure and new information obtained on the geological-field description of productive reservoirs, the current state of development, the study of the status and extent depletion of oil reserves from the reservoirs and issuing recommendations for monitoring and regulating the development process. This paper analyses geophysical, hydrodynamic studies of boreholes and reservoirs, the results of field studies, the current state of development. Initial data for assessing the development efficiency taking into consideration the history of borehole operation is determined, the effectiveness of the applied system for monitoring the development process and the state of the stock of producing wells is assessed, the effectiveness of measures to regulate the development process is analysed, and the effectiveness of the development process is assessed.

Continuous time-varying Q-factor estimation method in the time-frequency domain

The Q-factor is an important physical parameter for characterizing the absorption and attenuation of seismic waves propagating in underground media, which is of great significance for improving the resolution of seismic data, oil and gas detection, and reservoir description. In this paper, the local centroid frequency is defined using shaping regularization and used to estimate the Q values of the formation. We propose a continuous time-varying Q-estimation method in the time-frequency domain according to the local centroid frequency, namely, the local centroid frequency shift (LCFS) method. This method can reasonably reduce the calculation error caused by the low accuracy of the time picking of the target formation in the traditional methods. The theoretical and real seismic data processing results show that the time-varying Q values can be accurately estimated using the LCFS method. Compared with the traditional Q-estimation methods, this method does not need to extract the top and bottom interfaces of the target formation; it can also obtain relatively reasonable Q values when there is no effective frequency spectrum information. Simultaneously, a reasonable inverse Q filtering result can be obtained using the continuous time-varying Q values.

Geological characterization of the Patterson CO2 storage site from 3-D seismic data


 Approximately 26 square miles of new 3-D seismic data were acquired in July 2019 over the Patterson Site (Kearny County, Kansas) to assess its potential for carbon dioxide (CO2) storage. Seismic interpretation revealed that the Patterson Site contains multiple structural closures that lie on uplifted fault blocks, bounded by two reverse faults that strike nearly perpendicular to each other. These faults offset Precambrian through Pennsylvanian sections, including several primary reservoir and seal intervals. Fault displacements are maximum at the Precambrian basement and decrease upward. Data indicated a range of structural and combination traps exists at the Patterson Site in the Cambrian-Ordovician Arbuckle through Mississippian Osagian reservoirs. The three-way closures along the NW–SE fault have structural relief of ~130 ft (40 m), and the four-way closures contain relief of ~60 ft (18 m). Erosional surfaces and multiple basement fractures also are observed on the top of the Precambrian. A Mississippian-aged incised valley system also was observed at the Patterson Site. The incised valleys formed during the Meramecian-Chesteran Stages with an incised depth up to 250 ft (76 m). The motion of the reverse faults likely captured existing meandering and linear channels, causing the current deeply incised morphology. The incised valleys observed at Patterson are similar in age, structural style, shape, incision depth, and seismic attribute properties to incised valleys observed by other workers at Pleasant Prairie South, Eubank, and Shuck oil fields (southwest Kansas). Further research should focus on estimating reactivation tendency and sealing characteristics of the reverse faults to evaluate the seal integrity of the saline reservoirs. This will reduce uncertainty concerning the risk of CO2 migration during injection and storage. Further reservoir description, modeling, and simulation are also underway to characterize the storage potential at the Patterson Site.

Connotations of scientific development of giant gas fields in China

A giant gas field refers to a gas field with proved geological reserves of natural gas more than 30 billion cubic meters, peak annual output of natural gas more than 1 billion cubic meters, and a certain plateau period. It is an important foundation for the rapid growth of China's natural gas reserves and production and its long-term stable development in the future. And it is also the key to ensure the security of gas supply. By systematically analyzing the development practice of more than 260 gas fields at home and abroad and dissecting and simulating typical gas fields, this paper comprehensively studied the connotation, core technology and whole life cycle index system on the scientific development of giant gas fields. It is indicated that the scientific development connotation of giant gas fields consists of the following parts. First, the natural gas development concept with “water identification, control and treatment” as the technical system is put forward. That is to carry out “water-control development” in conventional gas fields and hydraulic fracturing development in unconventional gas fields. A reasonable gas production rate should be set according to the types and characteristics of gas reservoirs. Second, economic and social benefits of gas field development should be evaluated comprehensively to ensure its long-term stable production. Third, the suitable production stabilization mode shall be adopted according to the production characteristics of gas wells and the development characteristics of reservoirs. Fourth, the recoverable reserves of a gas field, the development laws of formation water and artificial water flooding, the ultimate recovery technological countermeasures of the gas field, the cost-effective stable period of the gas field should be determined based on the fine description of conventional and unconventional gas reservoirs in different stages, the development characteristics of gas reservoirs and the monitoring results of production performance. The core technologies in the scientific development of giant gas fields include large-scale optimization technology, scientific well arrangement technology, balanced production technology and deep potential tapping technology. Based on this, several parameters are selected comprehensively as the key indexes for the scientific development of giant gas fields, such as production rate, pressure drop, degree of reserves recovery and output per unit pressure drop, and the whole life cycle index systems for four types of gas reservoirs are established, i.e., high-pressure gas reservoirs, low-permeability and tight gas reservoirs, fractured–porous reservoirs, and shale gas reservoirs. In conclusion, the research results enrich and improve the theories of natural gas development geology and are conducive to guiding the scientific development of different-type giant gas fields, thus further promoting the rapid development of natural gas industry in China.

Reservoir Description Based on Computer Comprehensive Application of Seismic Attributes and Seismic Inversion

Oil field exploration is a common phenomenon of geographical observation in daily life. However, the areas where oil fields appear are always unlucky. There are always various problems in its structure. People don’t want to encounter the difficult reservoir problems in the process of oilfield exploitation[1]. It will affect the economic development and technological progress of the oilfield development process in our society. On this basis, the related problems of computer-based earthquake prediction have gradually attracted the attention of some experts in the academic field. The structure of the study area where earthquakes often occur is complex, and the lateral structure of its reservoir changes greatly. Therefore, it is urgent to analyze the seismic attributes and seismic inversion by computer.

Integrating machine learning and data analytics for geostatistical characterization of clastic reservoirs

An integrated multidisciplinary workflow of machine learning and data analytics was conducted for the multivariate geostatistical characterization of clastic reservoirs. This workflow was adopted for the estimation of lithofacies and permeability distributions in non-core intervals and wells on data from the upper shale member/Zubair formation in Luhais oil field, southern Iraq. Specifically, the workflow includes field and reservoir description, review of univariate, bivariate, and multivariate statistics for facies and petrophysical property distribution. In univariate and bivariate statistical analysis, the correction of core and log scale was adopted in order to have core permeability corrected to log scale. The discrete lithofacies distribution has been obtained for a few wells to be later incorporated in the permeability modeling and prediction.At first, the measured discrete lithofacies distributions were incorporated into classification algorithms to model the facies given the well logging data (shale volume, neutron porosity and water saturation) in order to predict the discrete facies distribution at other wells of missing records. The Multinomial Logistic Regression (Multinom), Logistic Boosting Regression (LogitBoost), and Extreme Gradient Boosting (XGBoost) were all comparatively adopted for lithofacies classification. After that, the resulting most accurate discrete facies distribution by LogitBoost were included along with the well logging interpretations into the multivariate permeability modeling through advanced machine learning approaches to model and predict the corrected core permeability given well logging interpretations for all wells in the reservoir. More specifically, the Multivariate Adaptive Regression Splines (MARS) and Smooth Generalized Additive Models (SGAM) were comparatively adopted to model and predict the core permeability at all wells. From this workflow, it was indicated that LogitBoost and MARS were the best machine learning algorithms to produce the most realistic lithofacies classification and permeability modeling, respectively.

Reservoir formation conditions and key technologies for exploration and development in Shengtuo oilfield in Bohai Bay Basin

The Shengtuo oilfield is the largest monolithic oilfield in the Bohai Bay Basin and even in the continental fault basins of Eastern China. Since discovered in 1963, the Shengtuo oilfield experiences the early exploration stage of rapid reserve discovery and production, the middle exploration stage of high and stable production and development, and the late exploration stage of theoretical and technological innovation for expanding the field of exploration, research on fine development technology of high-water-cut reservoir for enhanced oil recovery, and maintaining slow decline of oil production. Reserves of this oilfield increase in each stage. By the end of 2018, after more than 50 years of exploration and development, the Shengtuo oilfield has cumulative proven oil reserves of 0.512 × 109t, cumulative oil production of 0.193 × 109t, and it keeps stable annual production of more than 1.50 × 106t in 2018. Abundant hydrocarbon resources, large anticline structure near to oil source, multiple types of reservoirs and traps, and favorable hydrocarbon migration channels provide favorable hydrocarbon accumulation conditions for the Shengtuo oilfield. Fine development techniques such water-flooding in the early development stage, fine reservoir description in the high water-cut stage, stratigraphic subdivision and well pattern vector adjustment, tapping remaining oil in rhythmic layers, etc., as well as continuous progress in exploration technologies of glutenite and turbidite reservoirs around the main body of the oilfield in the high-level exploration stage, provide effective supports for continuous increase of reserves and long-term high and stable production of the Shengtuo oilfield.

The value of high-density blended OBN seismic for drilling and reservoir description at the Tangguh gas fields, Eastern Indonesia

The Tangguh gas fields in Eastern Indonesia are overlain by a complex overburden, including a thick, heavily faulted, and intensely karstified carbonate interval that tends to scatter and attenuate seismic energy. Development drilling is challenging, with the potential for pack-offs and stuck pipe when drilling into unstable, partially collapsed caves or karstified fault planes while on total losses. Ideally, these karst features are to be avoided when planning and drilling wells, but avoiding them depends on having a well-resolved seismic image. Historical towed-streamer and sparse ocean-bottom cable seismic is low fold and does not give a satisfactory image for well planning. Advances in ocean-bottom node technology, computer processing, and capacity coupled with efficient survey design and blended acquisition utilizing multiple source vessels allowed a step change in data density. This provided a new high-quality seismic image to support future development activities. The advantages of densely sampled, full-azimuth data include rapid delivery of fast-track products (because high-quality images can be constructed with relatively simple processing flows), greatly improved overburden imaging, and a corresponding uplift in deeper imaging leading to enhanced reservoir characterization.

New method of tapping potential of remaining oil after chemical flooding

In the study area, the chemical flooding type II oil layer and the water flooding type III oil layer exhibited alternating distribution in the horizon. The recovery factor during the chemical flooding stage was relatively low, at 7.44%, and had the potential for mining. Subsequent water flooding phases cannot use conventional potential tapping methods. It is necessary to further explore new targeted potential tapping methods and explore new ways for more cost-effective development. This study makes full use of dynamic data, fine reservoir description data and numerical modeling data to identify the remaining oil in the subsequent waterflooding stage, through coordinated adjustment of well patterns in the second and third types of oil layers, using unblocking, periodic production, reduced-speed oil production, and rotation Use wells and other methods to comprehensively tap potential and tap remaining oil.

Developed correlations between sound wave velocity and porosity, permeability and mechanical properties of sandstone core samples

In the evaluation of a petroleum reserve, it is necessary to accurately determine certain petrophysical properties such as porosity and permeability of the reservoir rocks and rock mechanical properties. Petrophysical properties are key factors in the reservoir description and geomechanical properties are determining parameters in drilling operations and in stimulation and hydraulic fracturing jobs and also the development plans for given reservoir. It is more convenient to use homogenous rock samples with nearly constant initial permeability; however, obtaining such cores is very difficult. In this paper a simulated natural and homogeneous compacted sandstone rock with known physical and petrophysical properties were used. Physical properties of reservoir rocks include pore size distribution; grain size, cementing material concentration, and confining pressure affect rock porosity and permeability. Sound wave velocity was measured using an ultra sound tool on different sandstone core samples. Good correlations have been developed between sound wave velocity (VP and VS) the petrophysical properties mainly porosity and permeability. Significant correlations have been found between seismic wave velocity (VP, VS and VP/VS) and mechanical parameters namely Young’s modulus, shear modulus, bulk modulus and Poison’s ratio for sandstone core samples. This study has been carried out on dry core samples and core samples with different water saturations and results showed that there are changes in the correlation between seismic wave velocity and mechanical and petrophysical properties as function of water saturation change from dry rock to water wet rock.

Research on Fine Reservoir Distribution Based on Computer Modeling of Horizontal Well Data

With the decrease of oil reserves, the difficulty of oil reservoir exploitation is increasing day by day. In this case, the development of oil reservoir research is becoming more and more important. There are more complicated continental reservoirs in Chinese oilfields, which makes the development of oilfields more difficult. The traditional way of describing oilfields can no longer accurately describe the complex petroleum geological environment. Therefore, a more efficient and detailed description of modern reservoirs is needed. Methods, using computer modeling to analyze horizontal wells and quantitatively study the reservoir conditions.

A multi-disciplinary approach to establish a workflow for the application of machine learning for detailed reservoir description — Wisting case study

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