Thermal Modeling and Inversion of Borehole Temperature Data

Unter den erneuerbaren Ressourcen nimmt die Geothermie aufgrund ihrer Grundlastfähigkeit eine einzigartige Stellung ein. Bei der Gewinnung von geothermischen Energiequellen ist einer der entscheidenden Parameter die Formationstemperatur, die das thermische Potenzial des geothermischen Systems und die installierte Leistung der erzeugten Energie bestimmt. Um Hochtemperaturzonen zu erreichen, werden Tiefbohrungen mit fortschrittlichen Technologien wie dem Enhanced Geothermal System durchgeführt. Insbesondere die hohe Produktivität und Effizienz der Stromerzeugung aus superkritischen geothermischen Ressourcen haben in den letzten Jahren Initiativen motiviert, in ultraheiße Magmakammern zu bohren. Da die Formationstemperatur während der Bohrung jedoch in der Regel stark gestört wird, müssen die Temperaturdaten aus Bohrlöchern interpretiert werden, um die wahre Formationstemperatur im thermischen Gleichgewicht zu bestimmen, die auch als statische Formationstemperatur (SFT) bezeichnet wird. Der herkömmliche Ansatz zur Bestimmung der SFT besteht darin, Korrekturen an den Temperaturdaten vorzunehmen, die während eines ausreichenden Zeitraums nach Beendigung der Bohrung (auch Shut-in genannt) gemessen wurden. Die Anwendung einer solchen Methode kann jedoch in Hochenthalpie-Bohrlöchern auf mehrere Herausforderungen stoßen, einschließlich wirtschaftlicher und technischer Beschränkungen bei der Durchführung von Hochtemperaturmessungen während langer thermischer Erholungsphasen und Sicherheitsfragen, wenn Verrohrung und Instrumente hohen Temperaturen ausgesetzt sind. In einem solchen Zusammenhang kann es notwendig sein, andere Techniken zur Interpretation von Temperaturdaten zu verwenden, die unter einer anderen Strömungsbedingung, wie z.B. der Injektion, gemessen wurden, um diese Einschränkungen bei der Datenerfassung zu überwinden. Diese Arbeit befasst sich mit der Bestimmung der SFT von Hochtemperaturbohrungen durch Anwendung numerischer Modellierungs- und Inversionstechniken auf Temperaturdaten, die unter Kühlbedingungen (oder unterkritischen Bedingungen) gewonnen wurden. Hintergrund ist das Island Deep Drilling Project, bei dem eine alte Produktionsbohrung (RN-15) zu einer neuen Explorationsbohrung (RN-15/IDDP-2) vertieft wird. Während der Bohrung trat ein ernstes Problem auf: der hohe Zirkulationsverlust in mehreren Verlustzonen. Die Messdaten zeigen, dass die Flüssigkeit am Boden des Bohrlochs einen überkritischen Zustand erreicht hat, auch wenn sich das thermische Feld nicht im Gleichgewicht befindet. Da diese Daten gemessen werden, während noch kaltes Wasser in das Bohrloch injiziert wird, stellt sich die Frage, ob es möglich ist, die SFT und unbekannte Strömungsverluste aus den Temperaturlogs der Injektion zu bestimmen. Vor diesem Hintergrund habe ich in dieser Dissertation zunächst Simulationsstudien durchgeführt, um unser Verständnis der thermischen Prozesse beim Bohren und Loggen zu verbessern. Anschließend habe ich Inversionsarbeitsabläufe entwickelt, die sowohl eine strenge Quantifizierung der Unsicherheiten in den Schätzungen als auch die Berechnung der posterioren Wahrscheinlichkeitsdichtefunktion für die SFT-Schätzungen ermöglichen. In der ersten Studie werden zunächst die Faktoren und Prozesse untersucht, die die Temperaturentwicklung im Bohrloch während der Injektions- und Shut-in-Perioden beeinflussen. Bei der thermischen Modellierung von Temperaturlogs werden verschiedene Bohrszenarien für Bohrungen mit hoher Enthalpie berücksichtigt, z.B. Injektions- und Shut-in-Bedingungen in mehreren Verrohrungssträngen und das Vorhandensein von Strömungsverlusten. In der frühen Übergangsphase des Shut-in ist die Bohrlochtemperatur sehr empfindlich gegenüber der Wärmeübertragungsrate zwischen der Bohrlochflüssigkeit und der festen Wand. Insbesondere wird die Rolle der freien Konvektion hervorgehoben, indem gezeigt wird, dass die richtige Parametrisierung der Wärmeübertragungsrate durch freie Konvektion den Wert der vorhergesagten Bohrlochtemperatur erheblich beeinflusst. Andererseits ist die Bohrlochtemperatur bei der Flüssigkeitsinjektion stark von der Durchflussrat abhängig. Diese Abhängigkeit ermöglicht die Verwendung eines Injektions-Temperaturprotokolls zur Identifizierung und Quantifizierung von Strömungsverlusten im Bohrloch. Schließlich wird die SFT aus den Temperaturlogs mit Hilfe der Horner-Plot-Methode abgeleitet. Die Ergebnisse weisen auf zwei Probleme hin: die Interpretation von Temperaturlogs, die während des Shut-in aufgezeichnet wurden, würde Daten erfordern, die nach einer langen Shut-in-Periode gemessen wurden; die Kühlung des Bohrlochs kann große Fehler bei der Schätzung der SFT verursachen. Diese Aspekte machen deutlich, dass die Anwendung der Horner-Plot-Methode in Hochtemperaturbohrlöchern sehr schwierig sein kann

Fractal Analysis and Chaos in Geosciences

The fractal analysis is becoming a very useful tool to process obtained data from chaotic systems in geosciences. It can be used to resolve many ambiguities in this domain. This book contains eight chapters showing the recent applications of the fractal/mutifractal analysis in geosciences. Two chapters are devoted to applications of the fractal analysis in climatology, two of them to data of cosmic and solar geomagnetic data from observatories. Four chapters of the book contain some applications of the (multi-) fractal analysis in exploration geophysics. I believe that the current book is an important source for researchers and students from universities

Imaging of a fluid injection process using geophysical data - A didactic example

In many subsurface industrial applications, fluids are injected into or withdrawn from a geologic formation. It is of practical interest to quantify precisely where, when, and by how much the injected fluid alters the state of the subsurface. Routine geophysical monitoring of such processes attempts to image the way that geophysical properties, such as seismic velocities or electrical conductivity, change through time and space and to then make qualitative inferences as to where the injected fluid has migrated. The more rigorous formulation of the time-lapse geophysical inverse problem forecasts how the subsurface evolves during the course of a fluid-injection application. Using time-lapse geophysical signals as the data to be matched, the model unknowns to be estimated are the multiphysics forward-modeling parameters controlling the fluid-injection process. Properly reproducing the geophysical signature of the flow process, subsequent simulations can predict the fluid migration and alteration in the subsurface. The dynamic nature of fluid-injection processes renders imaging problems more complex than conventional geophysical imaging for static targets. This work intents to clarify the related hydrogeophysical parameter estimation concepts

Uncertainty and Explainable Analysis of Machine Learning Model for Reconstruction of Sonic Slowness Logs

Logs are valuable information for oil and gas fields as they help to determine the lithology of the formations surrounding the borehole and the location and reserves of subsurface oil and gas reservoirs. However, important logs are often missing in horizontal or old wells, which poses a challenge in field applications. In this paper, we utilize data from the 2020 machine learning competition of the SPWLA, which aims to predict the missing compressional wave slowness and shear wave slowness logs using other logs in the same borehole. We employ the NGBoost algorithm to construct an Ensemble Learning model that can predicate the results as well as their uncertainty. Furthermore, we combine the SHAP method to investigate the interpretability of the machine learning model. We compare the performance of the NGBosst model with four other commonly used Ensemble Learning methods, including Random Forest, GBDT, XGBoost, LightGBM. The results show that the NGBoost model performs well in the testing set and can provide a probability distribution for the prediction results. In addition, the variance of the probability distribution of the predicted log can be used to justify the quality of the constructed log. Using the SHAP explainable machine learning model, we calculate the importance of each input log to the predicted results as well as the coupling relationship among input logs. Our findings reveal that the NGBoost model tends to provide greater slowness prediction results when the neutron porosity and gamma ray are large, which is consistent with the cognition of petrophysical models. Furthermore, the machine learning model can capture the influence of the changing borehole caliper on slowness, where the influence of borehole caliper on slowness is complex and not easy to establish a direct relationship. These findings are in line with the physical principle of borehole acoustics

Uncertainty reduction in reservoir parameters prediction from multiscale data using machine learning in deep offshore reservoirs.

Developing a complete characterization of reservoir properties involved in subsurface multiphase flow is a very challenging task. In most cases, these properties - such as porosity, water saturation, permeability (and their variants), pressure, wettability, bulk modulus, Young modulus, shear modulus, fracture gradient - cannot be directly measured and, if measured, are available only at small number of well locations. The limited data are then combined with geological interpretation to generate a model. Also increasing the degree of this uncertainty is the fact that the reservoir properties from different data sources - like well logs, cores and well test - often produce different results, thus making predictions less accurate. The present study focussed on three reservoir parameters: porosity, fluid saturation and permeability. These were selected based on literature and sensitivity analysis, using Monte Carlo simulations on net present value, reserve estimates and pressure transients. Sandstone assets from the North Sea were used to establish the technique for uncertainty reduction, using machine learning as well as empirical models after data digitization and cleaning. These models were built (trained) with observed data using other variables as inputs, after which they were tested by then using the input variables (not used for the training) to predict their corresponding observed data. Root Mean Squared Error (RMSE) of the predicted and the actual observed data was calculated. Model tuning was done in order to optimize its key parameters to reduce RMSE. Appropriate log, core and test depth matching was also ensured including upscaling combined with Lorenz plot to identify the dominant flow interval. Nomographic approach involving a numerial simulation run iteratively on multiple non-linear regression model obtained from the dataset was also run. Sandstone reservoirs from the North Sea not used for developing the models were then used to validate the different techniques developed earlier. Based on the above, the degree of uncertainty associated with porosity, permeability and fluid saturation usage was demonstrated and reduced. For example, improved accuracies of 1-74%, 4-77% and 40% were achieved for Raymer, Wyllie and Modified Schlumberger, respectively. Raymer and Wyllie were also not suitable for unconsolidated sandstones while machine learning models were the most accurate. Evaluation of logs, core and test from several wells showed permeability to be different across the board, which also highlights the uncertainty in their interpretation. The gap between log, core and test was also closed using machine learning and nomographic methods. The machine learning model was then coded into a dashboard containing the inputs for its training. Their relationship provides the benchmark to calibrate one against the other, and also to create the platform for real-time reservoir properties prediction. The technology was applied to an independent dataset from the Central North Sea deep offshore sandstone reservoir for the validation of these models, with minimum tuning and thus effective for real-time reservoir and production management. While uncertainties in measurements are crucial, the focus of this work was on the intermediate models to get better final geological models, since the measured data were from the industry

Machine Learning Based Real-Time Quantification of Production from Individual Clusters in Shale Wells

Over the last two decades, there has been advances in downhole monitoring in oil and gas wells with the use of Fiber-Optic sensing technology such as the Distributed Temperature Sensing (DTS). Unlike a conventional production log that provides only snapshots of the well performance, DTS provides continuous temperature measurements along the entire wellbore. Whether by fluid extraction or injection, oil and gas production changes reservoir conditions, and continuous monitoring of downhole conditions is highly desirable. This research study presents a tool for real-time quantification of production from individual perforation clusters in a multi-stage shale well using Artificial Intelligence and Machine Learning. The technique presented provides continuous production log on demand thereby providing opportunities for the optimization of completions design and hydraulic fracture treatments of future planned wells. A Fiber-Optic sensing enabled horizontal well MIP-3H in the Marcellus Shale has been selected for this work. MIP-3H is a 28-stage horizontal well drilled in July 2015, as part of a Department of Energy (DOE)-sponsored project - Marcellus Shale Energy & Environment Laboratory (MSEEL). A one-day conventional production logging operation has been performed on MIP-3H using a flow scanner while the installed Fiber-Optic DTS unit has collected temperature measurements every three hours along the well since completion. An ensemble of machine learning models has been developed using as input the DTS measurements taken during the production logging operation, details of mechanical logs, completions design and hydraulic fracture treatments data of the well to develop the real-time shale gas production monitoring tool

An advanced computational intelligent framework to predict shear sonic velocity with application to mechanical rock classification

Shear sonic wave velocity (Vs) has a wide variety of implications, from reservoir management and development to geomechanical and geophysical studies. In the current study, two approaches were adopted to predict shear sonic wave velocities (Vs) from several petrophysical well logs, including gamma ray (GR), density (RHOB), neutron (NPHI), and compressional sonic wave velocity (Vp). For this purpose, five intelligent models of random forest (RF), extra tree (ET), Gaussian process regression (GPR), and the integration of adaptive neuro fuzzy inference system (ANFIS) with differential evolution (DE) and imperialist competitive algorithm (ICA) optimizers were implemented. In the first approach, the target was estimated based only on Vp, and the second scenario predicted Vs from the integration of Vp, GR, RHOB, and NPHI inputs. In each scenario, 8061 data points belonging to an oilfield located in the southwest of Iran were investigated. The ET model showed a lower average absolute percent relative error (AAPRE) compared to other models for both approaches. Considering the first approach in which the Vp was the only input, the obtained AAPRE values for RF, ET, GPR, ANFIS + DE, and ANFIS + ICA models are 1.54%, 1.34%, 1.54%, 1.56%, and 1.57%, respectively. In the second scenario, the achieved AAPRE values for RF, ET, GPR, ANFIS + DE, and ANFIS + ICA models are 1.25%, 1.03%, 1.16%, 1.63%, and 1.49%, respectively. The Williams plot proved the validity of both one-input and four-inputs ET model. Regarding the ET model constructed based on only one variable,Williams plot interestingly showed that all 8061 data points are valid data. Also, the outcome of the Leverage approach for the ET model designed with four inputs highlighted that there are only 240 "out of leverage" data sets. In addition, only 169 data are suspected. Also, the sensitivity analysis results typified that the Vp has a higher effect on the target parameter (Vs) than other implemented inputs. Overall, the second scenario demonstrated more satisfactory Vs predictions due to the lower obtained errors of its developed models. Finally, the two ET models with the linear regression model, which is of high interest to the industry, were applied to diagnose candidate layers along the formation for hydraulic fracturing. While the linear regression model fails to accurately trace variations of rock properties, the intelligent models successfully detect brittle intervals consistent with field measurements

Gas-hydrates saturation estimation in Krishna-Godavari basin, India

Gas hydrates are an unconventional energy resource. They may become an important source of energy for India in the future. They occur offshore along the continental margin. They are currently in exploratory and evaluation stages and their quantification is an important task. The goal of this thesis is to demonstrate a new technique for the estimation of gas hydrates volumes. The region of study is the Krishna-Godavari basin. It is located on the eastern offshore areas of India. The presence of gas hydrates has been proven by drilling into marine sediments as a part of the Indian National Gas Hydrates Program. Borehole subsurface and surface seismic data were collected during this expedition. I use a 2D seismic reflection line and borehole log data for my study. The method I use for estimation of gas hydrates saturation uses a combination of inversion of seismic reflection data and development of seismic attributes. My approach can be broadly described by following steps: 1. Process the seismic data to remove noise. Use stacked and migrated data along with well logs to perform poststack seismic inversion to obtain impedance information in volumetric portions of the subsurface. 2. Use NMO corrected CDP gather records of the seismic reflection data along with subsurface well logs to perform prestack seismic inversion to obtain impedance volumes. 3. Compare the results from step1 and step 2 and use the best results to perform multi-attribute analysis using a neural network method to predict resistivity and porosity logs at the well location. Use the transform equations obtained at the well location to predict the well logs throughout the seismic section in the desired zone of interest. 4. Use an anisotropic equivalent of Archie’s law that relates resistivity and porosity to saturation to predict saturation throughout the seismic reflection section. The majority of the previous work done in the region is limited to gas hydrates quantification only at the well location. By using neural networks for multi-attribute analysis, I have demonstrated a statistical based method for the prediction of log properties away from well location. My results suggest gas hydrates saturation in the range of 50-80% in the zone of interest. The estimated saturation of gas hydrates matches up very closely with the saturation estimates obtained from the cores recovered during coring of the boreholes. Hence my method provides a reliable method of quantification of gas hydrates by making best possible use of seismic and well log data. The unique combination of impedance derived attributes and neural-network includes the non-linear behavior in the predictive transform relationships. The use of an anisotropic formulation of Archie’s law to estimate saturation also produces accurate results confirmed with the observed gas-hydrates saturation.Geological Science

Proceedings of the joint Russian-American hydrogeology seminar

Hydrogeology research has been very active in both Russia and the US because of the concerns for migration of radioactive and chemical contaminants in soils and geologic formations, as well as for water problems related to mining and other industrial operations. Russian hydrogeologists have developed various analysis and field testing techniques, sometimes in parallel with US counterparts. These Proceedings come out of a Seminar held to bring together a small group (about 15) of active Russian researchers in geologic flow and transport associated with the disposal of radioactive and chemical wastes either on the soils or through deep injection wells, with a corresponding group (about 25) of American hydrogeologists. The meeting was intentionally kept small to enable informal, detailed and in-depth discussions on hydrogeological issues of common interest. Out of this interaction, the authors hope that, firstly, they will have learned from each other and secondly, that research collaborations will be established where there is the opportunity. This proceedings presents the summaries and viewgraphs from the presentations. What cannot be conveyed here is the warm and cooperative atmosphere of these interactions, both inside and outside the formal sessions, which may well lead to future collaborations