Understanding the Facies Architecture of a Fluvial-Aeolian of Tensleep Formation Using a Machine Learning Approach

2021 ◽  
Author(s):  
Lilik Tri Hardanto
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Seismic Inversion ◽  
Gas Production ◽  
Geometrical Shape ◽  
Facies Architecture ◽  
Hydrocarbon Reservoir ◽  
Teapot Dome ◽  
Resolution Graph ◽  
Tensleep Formation

Abstract Many aeolian dune reservoirs are built from various dune types, and many may remain unrecognized in subsurface work. The challenge is to tackle the complex geological architecture of dune types within the Teapot Dome dataset caused by wind and water erosion. Machine Learning (ML) helps predict facies architecture away from boreholes using seismic attributes and facies logs. It provides a detailed understanding of the facies architecture analysis of the relationship between the fluvial–aeolian environment in Tensleep Formation based on seismic and well data. It allows operators to wisely assess their hydrocarbon reservoir, improve safety, and maximize oil and gas production investment. The data from the Teapot Dome field (Naval Petroleum Reserve No.3 - NPR-3) provides a good testing ground for Machine Learning, as it is easy to validate and prove its value. This study will show how the ML supervised learning method incorporating Neural Network Seismic Inversion (NNSI) can successfully create porosity log and facies volumes. Moreover, unsupervised learning using Multi-Resolution Graph-based clustering (MRGC) can be used to classify the facies logs. NNSI has 0.963 for the cross-correlation coefficients for all wells. The ML approach was used to help recognize the type of aeolian dune reservoirs in the subsurface and correlate the well log and facies volumes. In addition, ML allowed the distinct sequences and reconstruction of their depositional history in the Tensleep Formation. This study also refers briefly to other examples of fluvial-aeolian facies architecture worldwide. It successfully found the ancient model in an existing modern fluvial-aeolian environment, revealing hidden information about facies architecture based on the geometrical shape of geobodies in the oil-producing reservoir in the Tensleep Formation.

scholarly journals Late Cretaceous basin development of the southern Danish Central Graben

1969 ◽  
Vol 20 ◽  
pp. 15-18
Author(s):  
Finn Jakobsen ◽  
Claus Andersen
Keyword(s):  
Late Cretaceous ◽  
Oil And Gas ◽  
Seismic Inversion ◽  
Gas Production ◽  
Structural Development ◽  
Oil Accumulation ◽  
Oil And Gas Production ◽  
North West ◽  
The North ◽  
Basin Development

The Danish oil and gas production mainly comes from fields with chalk reservoirs of Late Cretaceous (Maastrichtian) and early Paleocene (Danian) ages located in the southern part of the Danish Central Graben in the North Sea. The area is mature with respect to exploration with most chalk fields located in structural traps known since the 1970s. However, the discovery by Mærsk Oil and Gas A/S of the large nonstructurally and dynamically trapped oil accumulation of the Halfdan Field in 1999 north-west of the Dan Field (e.g. Albrechtsen et al. 2001) triggered renewed exploration interest. This led to acquisition of new high quality 3-D seismic data that considerably enhanced imaging of different depositional features within the Chalk Group. Parallel to the endeavours by the operator to locate additional non-structural traps in porous chalk, the Geological Survey of Denmark and Greenland took advantage of the new data to unravel basin development by combining 3-D seismic interpretation of a large number of seismic markers, well log correlations and 2-D seismic inversion for prediction of the distribution of porous intervals in the Chalk Group. Part of this study is presented by Abramovitz et al. (in press). In the present paper we focus on aspects of the general structural development during the Late Cretaceous as illustrated by semi-regional time-isochore maps. The Chalk Group has been divided into two seismically mappable units (a Cenomanian–Campanian lower Chalk Unit and a Maastrichtian–Danian upper Chalk Unit) separated by a distinct basin-wide unconformity.

scholarly journals Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1293
Author(s):  
Shamil Islamov ◽  
Alexey Grigoriev ◽  
Ilia Beloglazov ◽  
Sergey Savchenkov ◽  
Ove Tobias Gudmestad
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Oil And Gas ◽  
New Technologies ◽  
Learning Algorithms ◽  
Gas Production ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Drilling Process ◽  
Well Drilling

This article takes an approach to creating a machine learning model for the oil and gas industry. This task is dedicated to the most up-to-date issues of machine learning and artificial intelligence. One of the goals of this research was to build a model to predict the possible risks arising in the process of drilling wells. Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays, companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work describes a comparison of machine learning algorithms for anomaly detection during well drilling. In particular, machine learning algorithms will make it possible to make decisions when determining the geometry of the grid of wells—the nature of the relative position of production and injection wells at the production facility. Development systems are most often subdivided into the following: placement of wells along a symmetric grid, and placement of wells along a non-symmetric grid (mainly in rows). The tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we used historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. The experiential results illustrate that a model based on gradient boosting can classify the complications in the drilling process better than other models.

scholarly journals Impact of Diagenesis on the Reservoir Properties of the Cretaceous Sandstones in the Southern Bredasdorp Basin, Offshore South Africa

Minerals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 757
Author(s):  
Temitope Love Baiyegunhi ◽  
Kuiwu Liu ◽  
Oswald Gwavava ◽  
Christopher Baiyegunhi
Keyword(s):  
Oil And Gas ◽  
Gas Production ◽  
Reservoir Quality ◽  
Reservoir Properties ◽  
Oil And Gas Production ◽  
Thin Sections ◽  
Hydrocarbon Reservoir ◽  
Porosity And Permeability ◽  
The Impact

The Cretaceous sandstone in the Bredasdorp Basin is an essential potential hydrocarbon reservoir. In spite of its importance as a reservoir, the impact of diagenesis on the reservoir quality of the sandstones is almost unknown. This study is undertaken to investigate the impact of digenesis on reservoir quality as it pertains to oil and gas production in the basin. The diagenetic characterization of the reservoir is based on XRF, XRD SEM + EDX, and petrographic studies of 106 thin sections of sandstones from exploration wells E-AH1, E-AJ1, E-BA1, E-BB1 and E-D3 in the basin. The main diagenetic processes that have affected the reservoir quality of the sandstones are cementation by authigenic clay, carbonate and silica, growth of authigenic glauconite, dissolution of minerals and load compaction. Based on the framework grain–cement relationships, precipitation of the early calcite cement was either accompanied or followed up by the development of partial pore-lining and pore-filling clay cements, particularly illite. This clay acts as pore choking cement, which reduces porosity and permeability of the reservoir rocks. The scattered plots of porosity and permeability versus cement + clays show good inverse correlations, suggesting that the reservoir quality is mainly controlled by cementation and authigenic clays.

ISCA: Quick Real-Time Sand Potential Prediction Simulator based on Critical Drawdown Failure and Machine Learning Model

2021 ◽  
Author(s):  
A. I. Biladi
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
High Efficiency ◽  
Early Stage ◽  
Gas Production ◽  
Control Analysis ◽  
Sand Production ◽  
Oil And Gas Production ◽  
Sand Control ◽  
Prediction Techniques

Sand production is almost an inevitable problem in oil and gas production facilities. As the reservoir depletes, sand grains from the reservoir begin to flow into the wellbore, this can cause serious problems to the wellbore. Excessive sand production can eventually plug and erode tubing, casing, flowlines, and surface equipment or even lead to formation collapse. In general, once sand production has occurred and if it is not handled properly it can end the production life of a reservoir and wells. This problem mostly occurs in mature fields with marginal economics for workover. The more reasonable option is to predict or mitigate the sand production, which can help identify the most economical way of sand control methods at the early stage. Many conventional sand prediction techniques have been developed which are based on field observation and experience, laboratory sand production experiments, and theoretical or numerical modeling. These conventional techniques have proven their effectiveness, but to achieve them can be time-consuming and costly. In this paper, we try to predict sand production with high efficiency and accuracy by using a quick simulator. Integrated Sand Control Analysis or ISCA is a simple simulator to help predict early sand production based on critical borehole and calculate critical drawdown pressure prediction. ISCA is supported by several mathematical models that function to predict various types of formation. Integrated with Machine Learning makes ISCA also compatible with big data analysis. The results in this study show that the combination of Machine Learning and analytical model can achieve accuracy above 90% based on the comparison of laboratory results with software predictions. With a high level of accuracy results this software can be considered as a reliable tool to predict and analyze sand production.

Machine Learning Applications to Predict Surface Oil Rates for High Gas Oil Ratio Reservoirs

2021 ◽  
pp. 1-19
Author(s):  
Ahmed Farid Ibrahim ◽  
Redha Al Dhaif ◽  
Salaheldin Elkatatny ◽  
Dhafer Al-Shehri
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Gas Production ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector ◽  
Critical Flow ◽  
Flow Conditions ◽  
Production Rates ◽  
Subcritical Flow

Abstract Well-performance investigation highly depends on the accurate estimation of its oil and gas flow rates. Testing separators and multiphase flow meters are associated with many technical and operational issues. Therefore, this study aims to implement the support vector machine (SVM), and random forests (RF) as machine learning (ML) methods to estimate the well production rate based on chokes parameters for high GOR reservoirs. Dataset of 1,131 data points includes GOR, upstream and downstream pressures (PU, and PD), choke size (D64), and actual data of oil and gas production rates. The data have GOR was up to 9,265 SCF/STB, the oil rate varied from 1,156 and 7,982 BPD. SVM and RF models were built to estimate the production rates. The ML models were trained using seventy percent of the dataset, while the models were tested and validated using thirty percent of the dataset. The dataset was classified to 622 wells that were flowing at critical flow compared to 509 wells that were flowing at subcritical conditions based on a PD/PU ratio of 0.55. Four machine learning models were developed using SVM and RF for subcritical flow and critical flow conditions. Different performance indicators were applied to assess the developed models. SVM and RF models revealed average absolute percent error (AAPE) of 1.3, and 0.7%, respectively in the case of subcritical flow conditions. For critical flow conditions, the AAPE was found to be 1.7% in the SVM model, and 0.8% in the RF model. The developed models showed a coefficient of determination (R2) higher than 0.93. All developed ML models perform better than empirical correlations. These results confirm the capabilities to predict the oil rates from the choke parameters in real-time without the requirement of instrument installation of wellsite intervention.

Prediction of Reservoir Parameters Variation Based on Time-Lapse Seismic Survey

2012 ◽  
Vol 433-440 ◽  
pp. 6370-6374
Author(s):  
Gang Fang ◽  
Xiao Hong Chen ◽  
Jing Ye Li
Keyword(s):  
Oil And Gas ◽  
Time Lapse ◽  
Seismic Inversion ◽  
Gas Production ◽  
Physical Models ◽  
Seismic Survey ◽  
Dynamic Monitoring ◽  
Oil And Gas Production ◽  
Fluid Saturation ◽  
Unconsolidated Sand

Fluid saturation and pressure are two of most important reservoir parameters during oil and gas production scheme adjustment. A method to compute the change of fluid saturation and pressure with multi-parameters regression was presented based on time-lapse seismic inversion data. Rock physical models of unconsolidated sand rock reservoirs were determined according to the real field’s conditions to analyze how seismic attributes change with variation of reservoir parameters. The radial basis function artificial neural network which was trained by this model was used to predict saturation and effective pressure. The predicted results are of high consistency with reservoir numerical simulation, which provide valuable reference for reservoir dynamic monitoring.

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