Deep Learning Assisted Doppler Sensing for Hydrocarbon Downhole Flow Velocity Estimation

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Alberto Marsala ◽  
Virginie Schoepf ◽  
Linda Abbassi
Keyword(s):  
Deep Learning ◽  
Flow Velocity ◽  
Oil And Gas ◽  
Velocity Estimation ◽  
Gas Reservoirs ◽  
Hydrocarbon Production ◽  
Oil And Gas Exploration ◽  
Learning Framework ◽  
Measurement Results ◽  
Interpretative Framework

Abstract Logging hydrocarbon production potential of wells has been at the forefront of enhancing oil and gas exploration and maximize productivity from oil and gas reservoirs. A major challenge is accurate downhole fluid phases flow velocity measurements in production logging due to the criticality of mechanical spinner-based sensor devices. Ultrasonic Doppler based sensors are more robust and deployable either in wireline or logging while drilling (LWD) conditions; however, due to the different sensing physics, the measurement results may not be equivalent. We present in this work an innovative deep learning framework to estimate spinner phase velocities from Doppler based sensor velocities. Tests of the framework on a benchmark dataset displayed strong estimation results. This allows for the real-time automatic interpretative framework implementation and flow velocity estimations either in conventional wireline production logging technologies (PLTs) and potentially also in LWD conditions, when the well is flowing in underbalanced conditions.

scholarly journals Intelligent Identification Method of Sedimentary Microfacies Based on DMC-BiLSTM

2021 ◽  
Author(s):  
Ze Ren Luo ◽  
Yang Zhou ◽  
Yu Xing Li ◽  
Liang Guo ◽  
Juan Juan Tuo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Oil And Gas ◽  
Nonlinear Problems ◽  
Temporal Correlation ◽  
Learning Method ◽  
Correlation Clustering ◽  
Oil And Gas Exploration ◽  
Original Curve ◽  
Spatio Temporal ◽  
Sedimentary Microfacies

Sedimentary microfacies division is the basis of oil and gas exploration research. The traditional sedimentary microfacies division mainly depends on human experience, which is greatly influenced by human factor and is low in efficiency. Although deep learning has its advantage in solving complex nonlinear problems, there is no effective deep learning method to solve sedimentary microfacies division so far. Therefore, this paper proposes a deep learning method based on DMC-BiLSTM for intelligent division of well-logging—sedimentary microfacies. First, the original curve is reconstructed multi-dimensionally by trend decomposition and median filtering, and spatio-temporal correlation clustering features are extracted from the reconstructed matrix by Kmeans. Then, taking reconstructed features, original curve features and clustering features as input, the prediction types of sedimentary microfacies at current depth are obtained based on BiLSTM. Experimental results show that this method can effectively classify sedimentary microfacies with its recognition efficiency reaching 96.84%.

Prestack P-Wave Anisotropy Characteristic Analysis in EDA Media

2014 ◽  
Vol 556-562 ◽  
pp. 4647-4650
Author(s):  
Yong Wang
Keyword(s):  
Oil And Gas ◽  
Rapid Development ◽  
Fractured Reservoirs ◽  
P Wave ◽  
Characteristic Analysis ◽  
Gas Reservoirs ◽  
Oil And Gas Development ◽  
Oil And Gas Exploration ◽  
Development And Utilization ◽  
Oil And Gas Reservoirs

With the rapid development of China's national economy, oil and gas development and utilization of resources is also increasing, dwindling reserves of conventional oil and gas reservoirs. These inevitably lead to oil and gas exploration direction shifted gradually from shallow depth, by a conventional steering reservoir unconventional oil and gas reservoirs, fractured reservoirs will become the focus of the current oil and gas exploration areas. This paper studied the basic theory of fractured media, from the speed and the amplitude of pre-stack anisotropic characteristics are analyzed theoretically. Researches of these basic theories of EDA media provide a basis for the exploration of the fractured reservoirs.

Triassic volcanic-sedimentary strata in the basement of Songliao Basin, discovered by International Continental Scientific Drilling Borehole, SK-2

2021 ◽  
Author(s):  
Yongkang Yin ◽  
Pujun Wang ◽  
Youfeng Gao ◽  
Haibo Liu
Keyword(s):  
Inductively Coupled Plasma ◽  
Oil And Gas ◽  
Songliao Basin ◽  
Radiometric Dating ◽  
Gas Reservoirs ◽  
Scientific Drilling ◽  
Oil And Gas Exploration ◽  
Inductively Coupled ◽  
Icp Ms ◽  
Drilling Core

<p>In the Songliao Basin, the existence of lower Mesozoic strata remains a debatable issue. Previous studies indicated the absence of Triassic to Lower and Middle Jurassic strata in northeastern China because of uplift and erosion events associated with the return of geo-synclinal folds and orogenic movement during the Late Permian–Early Jurassic. To date, geochronological studies of intrusive and metamorphic rocks in the basement of the Songliao Basin have also confirmed Carboniferous, Permian, and Late Jurassic ages for the basement formations in general. In the International Continental Scientific Drilling Project (ICDP) in the Songliao Basin, radiometric dating has been carried out for the entire drilling core of the SK-2 east borehole. As a result, we have discovered Triassic volcanic-sedimentary strata in the basement of the Songliao Basin. Laser-ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) U–Pb geochronology was used in this research. Errors in individual analyses by LA-ICP-MS are given at the 1σ level, whereas errors in pooled ages are given at the 95% (2σ) confidence level. Triassic volcanic-sedimentary strata revealed by the SK-2 east borehole consist of andesitic volcanic breccias at the bottom; andesites, sandstones, and conglomerates in the middle; and andesites at the top. The total thickness of these strata is over 500 m. The formation age of the andesite at the depth of 6,031.9 m is 242.4 ± 2.1 Ma (MSWD = 0.06, n = 7). The youngest peak age of the sandstone at the depth of 6,286.2 m is 242.2 Ma. The formation age of the andesite at the depth of 6,286.2 m is 242.6 ± 1.5 Ma (MSWD = 1.02, n = 18). This study demonstrates that in the Songliao Basin, there are not only Carboniferous and Permian strata, but also a Triassic volcanic-sedimentary succession in the basement of the basin. The SK-2 drilling core reveals that this volcanic-sedimentary sequence has great thickness. These Triassic volcanic-sedimentary strata provide new clues for the study of the origin and development of the Songliao Basin. As both volcanic and sedimentary rocks can be oil and gas reservoirs, this discovery also provides a new target for oil and gas exploration deep in the Songliao Basin.</p>

scholarly journals Automated Classification of High-resolution Rock Image Based on Residual Neural Network

2021 ◽  
Vol 2095 (1) ◽  
pp. 012051
Author(s):  
Weibo Cai ◽  
Juncan Deng ◽  
Qirong Lu ◽  
Kengdong Lu ◽  
Kaiqing Luo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Resolution ◽  
Oil And Gas ◽  
Automated Classification ◽  
Maximum Probability ◽  
Rock Classification ◽  
Oil And Gas Exploration ◽  
Local Image

Abstract The identification and classification of high-resolution rock images are significant for oil and gas exploration. In recent years, deep learning has been applied in various fields and achieved satisfactory results. This paper presents a rock classification method based on deep learning. Firstly, the high-resolution rock images are randomly divided into several small images as a training set. According to the characteristics of the datasets, the ResNet (Residual Neural Network) is optimized and trained. The local images obtained by random segmentation are predicted by using the model obtained by training. Finally, all probability values corresponding to each category of the local image are combined for statistics and voting. The maximum probability value and the corresponding category are taken as the final classification result of the classified image. Experimental results show that the classification accuracy of this method is 99.6%, which proves the algorithm’s effectiveness in high-resolution rock images classification.

DOPPLER VS. SPINNER PLT SENSING FOR HYDROCARBON VELOCITY ESTIMATE BY DEEP-LEARNING APPROACH

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
◽  
Alberto Marsala ◽  
Virginie Schoepf ◽  
Linda Abbassi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Flow Velocity ◽  
Oil And Gas ◽  
Surface Flow ◽  
Sound Waves ◽  
Velocity Measurements ◽  
Data Set ◽  
Flowing Fluid ◽  
Flow Meters ◽  
Doppler Flow

Logging hydrocarbon production potential of wells has been at the forefront of enhancing oil and gas exploration and maximize productivity from oil and gas reservoirs. A major challenge is accurate downhole fluid phases flow velocity measurements in production logging (PLT) due to the criticality of mechanical spinner-based sensor devices. Ultrasonic Doppler-based sensors are more robust and deployable either in wireline or logging while drilling (LWD) conditions; however, due to the different sensing physics, the measurement results may vary. Ultrasonic Doppler flow meters utilize the Doppler effect that is a change in frequency of the sound waves that are reflected on a moving target. A common example is the change in pitch when a vehicle sounding a horn approaches and recedes from an observer. The frequency shift is in direct proportion of the relative velocity of the fluid with respect to the emitter-receiver and allows to infer the speed of the flowing fluid. Doppler flow meters offer many advantages over mechanical spinners such as the ability to measure without requiring calibration passes, the absence of mechanical moving parts, the sensors robustness to shocks and hits, easy installation and minimal affection by changes in temperature, density and viscosity of the fluid thus capability to work even in highly contaminated conditions such as tar, asphaltene deposits on equipment. Despite being widely used in surface flow metering, ultrasonic Doppler sensor applications to downhole environment have been so far very limited. We present in this work an innovative deep learning framework to estimate spinner phase velocities from Doppler based sensor velocities. Tests of the framework on a benchmark data set displayed strong estimation results, in particular outlining the ability to utilize Doppler-based sensors for downhole phase velocity measurements and allows the comparison of the estimates with previously recorded spinner velocity measurements. This allows for the real-time automated interpretative framework implementation and flow velocity estimations either in conventional wireline production logging technologies and potentially also in LWD conditions, when the well is flowing in underbalanced conditions.

scholarly journals Application of deep learning to large scale riverine flow velocity estimation

2021 ◽  
Author(s):  
Mojtaba Forghani ◽  
Yizhou Qian ◽  
Jonghyun Lee ◽  
Matthew W. Farthing ◽  
Tyler Hesser ◽  
...  
Keyword(s):  
Deep Learning ◽  
Flow Velocity ◽  
Large Scale ◽  
Velocity Estimation

scholarly journals Formation Conditions and Exploration Directions of Large Cretaceous Sub-salt Oil and Gas Reservoirs in Santos Basin

2020 ◽  
Vol 206 ◽  
pp. 01013
Author(s):  
KunQiang Jin ◽  
Yunfeng Zhang
Keyword(s):  
Oil And Gas ◽  
Gas Reservoirs ◽  
Oil And Gas Exploration ◽  
High Area ◽  
Formation Zone ◽  
Oil And Gas Resources ◽  
Storage Zone ◽  
Formation Conditions ◽  
The Rich ◽  
Type 3

The rich oil and gas resources and good reservoir-forming conditions in the Santos Basin in Brazil make it a majorstrategic succession area for oil and gas exploration in the Santos Basin. The sub-salt bio-reservoir-cap configuration in the SantosBasin can be divided into two types: bio-reservoir-cap superposition and bio-reservoir superposition; the preservation conditions canbe divided into cap-slip-off extension deformation type, and the cap-layer is strongly extruded Deformation type, 3 types of capping stable extrusion deformation type; reservoir formation zone can be divided into 2 types: subsalt raw salt storage and subsalt raw salt storage. The high area outside the Santos Basin in the sub-salt source-salt storage zone is a favorable exploration direction for finding large oil and gas areas under the salt in the Santos Basin.

scholarly journals Recognition of Oil and Gas Reservoir Space Based on Deep Learning

2021 ◽  
Vol 267 ◽  
pp. 01038
Author(s):  
Shasha Yang ◽  
Anjie Jin ◽  
Yongfu Xu
Keyword(s):  
Deep Learning ◽  
Oil And Gas ◽  
Ordos Basin ◽  
Learning Technology ◽  
Work Efficiency ◽  
Labor Costs ◽  
Gas Reservoir ◽  
Oil And Gas Exploration ◽  
The Ordos Basin ◽  
Oil And Gas Reservoir

The identification of oil and gas reservoir space is of great significance to oil and gas exploration. Deep learning technology represented by convolutional neural network is currently the most widely used artificial intelligence method in the field of image recognition. Using convolutional neural networks to identify the type and content of the reservoir space can not only ensure the objectivity and accuracy of the results, but also reduce labor costs and improve work efficiency. It has achieved good results in the identification of the reservoir space of the Chang 6 oil-bearing group in the Ordos Basin, which has a certain promotion significance.

Sri Lanka’s oil and gas plans are overambitious

2019 ◽  
Keyword(s):  
Renewable Energy ◽  
Energy Policy ◽  
Oil And Gas ◽  
Energy Resources ◽  
Renewable Energy Resources ◽  
Hydrocarbon Production ◽  
Content Type ◽  
Oil And Gas Exploration ◽  
Energy Needs

Subject Sri Lanka's plans to start hydrocarbon production. Significance Sri Lanka is aiming to start hydrocarbon production within four years. It currently relies on imports of oil and coal to meet its energy needs. Impacts A focus on oil and gas exploration will detract from development of renewable energy resources. Limited exploration success would mean long-term dependence on LNG imports. Adoption of a new gas policy may provide some certainty regarding the direction of the country’s energy policy.

scholarly journals Support Vector Machine Algorithm for Automatically Identifying Depositional Microfacies Using Well Logs

2019 ◽  
Vol 11 (7) ◽  
pp. 1919 ◽  
Author(s):  
Dahai Wang ◽  
Jun Peng ◽  
Qian Yu ◽  
Yuanyuan Chen ◽  
Hanghang Yu
Keyword(s):  
Support Vector Machine ◽  
Oil And Gas ◽  
Field Application ◽  
Well Logs ◽  
Support Vector ◽  
Gas Reservoirs ◽  
Oil And Gas Exploration ◽  
Core Samples ◽  
Svm Algorithm ◽  
The Cost

Depositional microfacies identification plays a key role in the exploration and development of oil and gas reservoirs. Conventionally, depositional microfacies are manually identified by geologists based on the observation of core samples. This conventional method for identifying depositional microfacies is time-consuming, and only the depositional microfacies in a few wells can be identified due to the limited core samples in these wells. In this study, the support vector machine (SVM) algorithm is proposed to identify depositional microfacies automatically using well logs. The application of SVM includes the following steps: First, the depositional microfacies are determined manually in several wells with core samples. Then, the training sets used in the SVM algorithm are extracted from the well logs. Finally, a quantitative discrimination model based on the SVM algorithm is established to realize the classification of depositional microfacies. Field application shows that this innovative and constructive solution can be effectively used in uncored wells to identify depositional microfacies with a rate of accuracy approaching 84%. It overcomes the limitation of the conventional manual method which greatly contributes to the cost-saving of core analysis and improves the sustainable profitability of oil and gas exploration.

Sign in / Sign up

Export Citation Format

Share Document