Copyright 2019 held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors. ABSTRACT Today, many machine learning techniques are regularly employed in petrophysical modelling such as cluster analysis, neural networks, fuzzy logic, self-organising maps, genetic algorithm, principal component analysis etc. While each of these methods has its strengths and weaknesses, one of the challenges to most of the existing techniques is how to best handle the variety of dynamic ranges present in petrophysical input data. Mixing input data with logarithmic variation (such as resistivity) and linear variation (such as gamma ray) while effectively balancing the weight of each variable can be particularly difficult to manage. DTA is conceived based on extensive research conducted in the field of CFD (Computational Fluid Dynamics). This paper is focused on the application of DTA to petrophysics and its fundamental distinction from various other statistical methods adopted in the industry. Case studies are shown, predicting porosity and permeability for a variety of scenarios using the DTA method and other techniques. The results from the various methods are compared, and the robustness of DTA is illustrated. The example datasets are drawn from public databases within the Norwegian and Dutch sectors of the North Sea, and Western Australia, some of which have a rich set of input data including logs, core, and reservoir characterisation from which to build a model, while others have relatively sparse data available allowing for an analysis of the effectiveness of the method when both rich and poor training data are available. The paper concludes with recommendations on the best way to use DTA in real-time to predict porosity and permeability. INTRODUCTION The seismic shift in the data analytics landscape after the Macondo disaster has produced intensive focus on the accuracy and precision of prediction of pore pressure and petrophysical parameters.
Oil price forecasting has been shown to be challenging if not impossible for the long-term. However, the oil price has a major impact on Exploration and Production projects.
Historical Project Realized Oil Price (PROP) can be calculated for example projects by summing up the total project revenue using the actual oil prices and dividing through the total amount of oil produced. For different starting dates of example projects, the PROP changes. Determining the PROP for different starting times, a Cumulative Distribution Function (CDF) can be derived. Adjusting this CDF for expected "half cycle breakeven costs" for the low limit and demand considerations for the high case leads to a PROP range that can be used for future project evaluation.
Including PROP ranges into project evaluation allows for the selection of the most attractive development option, Value of Information analysis and project Probability of Economic Success (PES) calculation including oil price uncertainty.
Furthermore, using PROP ranges rather than oil price scenarios enables a distinction between short-term budget planning and long-term project development. For budget planning, a scenario approach is suggested while for long-term planning PROP ranges should be used. Applying long-term planning on PROP ranges leads to less fluctuation in staff planning and small annual adjustments in PROP range forecasting. Also, using PROP ranges results in increasing PES project hurdles at low oil prices and lower PES hurdles at high oil prices. Hence, at low oil prices the risk averseness of the company is increased. Another effect of using PROP ranges is that at high oil prices robustness of projects to low oil prices is included in the assessment.
To investigate the effect of PROP ranges on portfolio PES hurdles and project PES hurdles, a simplified linear-fit-model was developed. The results of the model showed that the project PES hurdles in a Value at Risk assessment can be determined applying the linear-fit-model to quantify the oil price dependency. The required individual project PES hurdles can be adjusted using the linear-fit-model to account for oil price uncertainty.
A high rate of penetration (ROP) is considered one of the most sought-after targets when drilling a well. While physics-based models determine the importance of drilling parameters, they fail to capture the extent or degree of influence of the interplay of the different dynamic drilling features. Parameters such as WOB, RPM, and flowrate, MSE, bit run distance, gamma ray for each rock formation in the Volve field in the North Sea were examined ensuring an adequate ROP while controlling the tool face orientation is quite challenging. Nevertheless, its helps follow the planned well trajectory and eliminates excessive doglegs that lead to wellbore deviations. Five different Machine Learning algorithms were preliminary implemented to optimize ROP and create a less tortuous borehole. The collected data was cleaned and preprocessed and used to structure and train Random Forest, Support Vector Regression, Ridge Regression, LASSO, and Gradient Boosting, XG boost among others and the appropriate hyperparameters were selected. A successful model was chosen based on maximized ROP, minimized deviation from planned trajectory, and lower CPF. An MAEP of 15% was achieved using GBM boost followed AdaBoost. The algorithms have demonstrated competence on the historical dataset, accordingly it will be further tested on blind data to serve as a real-time system for drilling optimization to enable a fully automated system.
Maintaining a stable borehole and optimizing drilling are still considered to be vital practice for the success of any hydrocarbon field development and planning. The present study deliberates a case study on the estimation of pore pressure and fracture gradient for the recently decommissioned Volve oil field at the North Sea. High resolution geophysical logs drilled through the reservoir formation of the studied field have been used to estimate the overburden, pore pressure, and fracture pressure. The well-known Eaton’s method and Matthews-Kelly’s tools were used for the estimation of pore pressure and fracture gradient, respectively. Estimated outputs were calibrated and validated with the available direct downhole measurements (formation pressure measurements, LOT/FIT). Further, shear failure gradient has been calculated using Mohr-Coulomb rock failure criterion to understand the wellbore stability issues in the studied field. Largely, the pore pressure in the reservoir formation is hydrostatic in nature, except the lower Cretaceous to upper Jurassic shales, which were found to be associated with mild overpressure regimes. This study is an attempt to assess the in-situ stress system of the Volve field if CO2 is injected for geological storage in near future.
Petrophysics is a pivotal discipline that bridges engineering and geosciences for reservoir characterization and development. New sensor technologies have enabled real-time streaming of large-volume, multi-scale, and high-dimensional petrophysical data into our databases. Petrophysical data types are extremely diverse, and include numeric curves, arrays, waveforms, images, maps, 3-D volumes, and texts. All data can be indexed with depth (continuous or discrete) or time. Petrophysical data exhibits all the "7V" characteristics of big data, i.e., volume, velocity, variety, variability, veracity, visualization, and value. This paper will give an overview of both theories and applications of machine learning methods as applicable to petrophysical big data analysis.
Recent publications indicate that petrophysical data-driven analytics (PDDA) has been emerging as an active sub-discipline of petrophysics. Field examples from the petrophysics literature will be used to illustrate the advantages of machine learning in the following technical areas: (1) Geological facies classification or petrophysical rock typing; (2) Seismic rock properties or rock physics modeling; (3) Petrophysical/geochemical/geomechanical properties prediction; (3) Fast physical modeling of logging tools; (4) Well and reservoir surveillance; (6) Automated data quality control; (7) Pseudo data generation; and (8) Logging or coring operation guidance.
The paper will also review the major challenges that need to be overcome before the potentially game-changing value of machine learning for petrophysics discipline can be realized. First, a robust theoretical foundation to support the application of machine leaning to petrophysical interpretation should be established; second, the utility of existing machine learning algorithms must be evaluated and tested in different petrophysical tasks with different data scenarios; third, procedures to control the quality of data used in machine leaning algorithms need to be implemented and the associated uncertainties need to be appropriately addressed. The paper will outlook the future opportunities of enabling advanced data analytics to solve challenging oilfield problems in the era of the 4th industrial revolution (IR4.0).
Pastusek, Paul (ExxonMobil Development Co.) | Payette, Greg (ExxonMobil Upstream Research Co.) | Shor, Roman (University of Calgary) | Cayeux, Eric (Norce) | Aarsnes, Ulf Jakob (Norce) | Hedengren, John (Brigham Young University) | Menand, Stéphane (DrillScan) | Macpherson, John (Baker Hughes GE) | Gandikota, Raju (MindMesh Inc.) | Behounek, Michael (Apache Corp.) | Harmer, Richard (Schlumberger) | Detournay, Emmanuel (University of Minnesota) | Illerhaus, Roland (Integrity Directional) | Liu, Yu (Shell Development Co.)
The drilling industry has substantially improved performance based on knowledge from physics-based, statistical, and empirical models of components and systems. However, most models and source code have been recreated multiple times, which requires significant effort and energy with little additional benefit or stepwise improvements. The authors propose that it is time to form a coalition of industry and academic leaders to support an open source effort for drilling, to encourage the reuse of continuously improving models and coding efforts. The vision for this guiding coalition is to 1) set up a repository for source code, data, benchmarks, and documentation, 2) encourage good coding practices, 3) review and comment on the models and data submitted, 4) test, use and improve the code, 5) propose and collect anonymized real data, 6) attract talent and support to the effort, and 7) mentor those getting started. Those interested to add their time and talent to the cause may publish their results through peer-reviewed literature.
This paper is based on the analysis of the ultrasonic/sonic data of the 9 5/8-in. casing logging of the 14 wells of the Varg field within the Norwegian Continental Shelf. While writing this papper Varg field was undergoing a plug and abandonment (P&A) phase after 19 years of production. High-quality bonding is observed behind the 9 5/8-in. casing far above expected theoretical top of cement within single casing areas. This bonding is attributed to the formation influence. Formation is used as an alternative to traditional cement barriers during P&A, and its use is regulated by the legislation.
The paper aims to develop better understanding of the mechanisms responsible for formation bonding development. The percentage of observed bonding at "high" and "high and moderate-to-high" quality is calculated within each well and is related to the various factors that could influence formation bonding development. Factors such as duration of time lapsed from well completion to well logging, type of well (producer versus injector), geological formation, type of drilling mud used, duration of production periods, volumes of production, and well deviation and azimuth were looked at to determine observable trends and relationships.
The results of the study allowed us to conclude which factors are critical or influence formation bonding. Based on the observations, recommendations can be made for the selection of the first well to be logged on the planned P&A campaigns. Correct selection of the first well saves time and resources on the formation testing for the qualification of the formation as a barrier.
The IADC and SPE are committed to delivering a balanced agenda around Diversity and Inclusion, to support member companies as they strive to address the gap in the Oil & Gas Sector. In 2019 the SPE/IADC International Drilling Conference and Exhibition in The Hague will host a session that allows delegates to explore the challenges facing the industry and hear firsthand, how it can be addressed. This initiative aims to build on the efforts already being undertaken at individual company levels to attract, develop and retain female staff - especially in technical and senior management roles, and to remove barriers that may currently hinder or discourage women from rising through the ranks into leadership roles. The aim is to address the factors contributing to the gender gap and to advantage all companies, their owners and shareholders through the incremental performance and value that parity will generate. This is good for our people, good for our stakeholders, and good for our business. Whilst in 2017 the session focused on subjects arising from DAVOS 2016 namely Leadership, Aspiration, goal setting, STEM, recruitment and retention, corporate culture and work life balance, the panel now feel it is time to move the conversation forward with some hard-hitting topics that affect the lives of many. Make sure you join us for this special session in The Hague.