Fluorescent Microspheres Method Efficiency in Horizontal Wells with Gas Condensate Liquid

The proportion of hard-to-recover reserves is currently increasing and reached more than 65% of total conventional hydrocarbon reserves. This results in an increasing number of horizontal wells put into operation. When evaluating the resource recovery efficiency in horizontal wells, and, consequently, the effectiveness of the development of gas condensate field, the key task is to evaluate the well productivity. To accomplish this task, it is necessary to obtain the reservoir fluid production profile for each interval. Conventional well logging methods with proven efficiency in vertical wells, in case of horizontal wells, will require costly asset-heavy applications such as coiled tubing, downhole tractors conveying well logging tools, and Y-tool bypass systems if pump is used. In addition, the logging data interpretation in the case of horizontal wells is less reliable due to the multiphase flow and variations of the fluid flow rate. The fluorescent-based nanomaterial production profiling surveillance technology can be used as a viable solution to this problem, which enables cheaper and more effective means of the development of hard-to-recover reserves. This technology assumes that tracers are placed downhole in various forms, such as marker tapes for lower completions, markers in the polymer coating of the proppant used for multi-stage hydraulic fracturing, and markers placed as fluid in fracturing fluid during hydraulic fracturing or acid stimulation during bottom-hole treatment. The fundamental difference between nanomaterial tracers production profiling and traditional logging methods is that the former offers the possibility to monitor the production at frac ports in the well for a long period of time with far less equipment and manpower, reduced costs, and improved HSE.

Well-Logging Prediction Based on Hybrid Neural Network Model

Well-logging is an important formation characterization and resource evaluation method in oil and gas exploration and development. However, there has been a shortage of well-logging data because Well-logging can only be measured by expensive and time-consuming field tests. In this study, we aimed to find effective machine learning techniques for well-logging data prediction, considering the temporal and spatial characteristics of well-logging data. To achieve this goal, the convolutional neural network (CNN) and the long short-term memory (LSTM) neural networks were combined to extract the spatial and temporal features of well-logging data, and the particle swarm optimization (PSO) algorithm was used to determine hyperparameters of the optimal CNN-LSTM architecture to predict logging curves in this study. We applied the proposed CNN-LSTM-PSO model, along with support vector regression, gradient-boosting regression, CNN-PSO, and LSTM-PSO models, to forecast photoelectric effect (PE) logs from other logs of the target well, and from logs of adjacent wells. Among the applied algorithms, the proposed CNN-LSTM-PSO model generated the best prediction of PE logs because it fully considers the spatio-temporal information of other well-logging curves. The prediction accuracy of the PE log using logs of the adjacent wells was not as good as that using the other well-logging data of the target well itself, due to geological uncertainties between the target well and adjacent wells. The results also show that the prediction accuracy of the models can be significantly improved with the PSO algorithm. The proposed CNN-LSTM-PSO model was found to enable reliable and efficient Well-logging prediction for existing and new drilled wells; further, as the reservoir complexity increases, the proxy model should be able to reduce the optimization time dramatically.

Time-Lapse Pulsed Neutron Well Logging in Oil Sands for Monitoring Steam Chamber Development

Steam-assisted gravity drainage (SAGD) technology, although a relatively new oil recovery method, has already proved its value in economic development of heavy-oil sands in Western Canada. The SAGD process requires a lifetime monitoring of steam chamber growth to optimize reservoir development, improve oil recovery, and minimize environmental impact. Operators have widely used pulsed neutron well logs to monitor their life cycles of oil sand reservoirs. Time-lapse pulsed neutron logs acquired in observation wells enable operators to effectively track the growth of the steam chamber and identify the changes of formation fluid saturations. We present high-temperature pulsed neutron logging technology and an algorithm to quantify steam, heavy oil and water saturations in SAGD wells. One of the major challenges in well logging operation is to withstand the thermal shock from the steam chamber. Reservoir temperature often varies abruptly, by as much as 250 degrees C in a very short interval, so the logging tool must be stable in drastic temperature variations. Well logging conditions such as a steam-filled wellbore, extra completion hardware and bad cement quality are challenging factors as well. Furthermore, formation fluid saturation analysis in Canadian oil sands is typically complex because the formation water salinity is relatively fresh but varies, clay properties are not homogeneous, and SAGD operations create conditions in which three-phase fluids coexist in the formation. These environmental conditions make it difficult to rely only on commonly used thermal neutron capture cross-section measurements (formation sigma). In this paper, case study examples present the above-mentioned challenges and solutions to identify the multi-component formation fluids. The multi-detector pulsed neutron well logging instrument has been modified with a custom-designed heat flask to handle the extreme temperature variations in the SAGD environment. This heat-flask equipped instrument ensures a stable data acquisition in the presence of rapid and extreme temperature variation and enables a prolonged and time-efficient operation through effective heat management. For saturation analysis, we demonstrate an advanced algorithm to quantify three fluid components using a combination of gamma ray ratio and carbon/oxygen (C/O) measurements.

Substantiation of paleohydrodynamic sedimentation modes and identification of missed oil-and-gas saturated areas that are promising for additional exploration of the Lower Cretaceous deposits of the Albian stage of the Achikulak oil field according to geophysical well logging data

In the development of the oil and gas complex, the improvement of methods for studying the patterns of distribution of hydrocarbon deposits is of particular importance. At the same time, scientific research can be carried out in different directions. These include increasing the resolution of various methods of borehole and areal geological and geophysical studies of lithological features and oil-and-gas content, developing new approaches to interpreting well logging results, analyzing the productivity of poorly studied regions and areas of the section, identifying low-amplitude folds and non-anticlinal deposits. The development of hydrocarbon deposits in the Stavropol Territory has been going on for many decades, which has led to reduction of deposits within medium and large anticlinal uplifts. Therefore, the main attention is now paid to the prospect for low-amplitude and small-size uplifts and traps of the non-anticlinal type, which include lithological and stratigraphic ones.

Assessment of the facial affiliation of local oil and gas objects based on geophysical data

In the established practice of geological exploration for oil and gas conclusions about the facies belonging of the rocks of oil and gas basins and individual exploration areas were made mainly on the basis of the study of core material. Recently for this purpose the results of seismic exploration and well logging have been used. Geophysical methods despite their obvious progress are indirect and intermittent core sampling and incomplete coring make facies analysis difficult. Тhe study of cuttings during the well logging process makes it possible to fill this gap through direct continuous observations along the well section. The use of the whole complex of geophysical methods allows one to mutually compensate for the limitations and disadvantages of each of them and more fully and reliably assess the genetic characteristics and reservoir potential of productive deposits. Sequential accumulation, comparison and analysis of heterogeneous geophysical data make it possible to continuously refine apriori facies models and forecast oil and gas content which ultimately allows to optimize the directions and volumes of drilling.

The Interpretation Technique of Rate Transient Analysis Data in Fractured Horizontal Wells

This research presents a modified approach to the data interpretation of Rate Transient Analysis (RTA) in hydraulically fractured horizontal well. The results of testing of data interpretation technique taking account of the flow allocation in the borehole according to the well logging and to the injection tests outcomes while carrying out hydraulic fracturing are given. In the course of the interpretation of the field data the parameters of each fracture of hydraulic fracturing were selected with control for results of well logging (WL) by defining the fluid influx in the borehole.