Neural network applications to reservoirs: Physics-based models and data models

International audienceEditoria

Machine Learning and Multi-Agent Systems in Oil and Gas Industry Applications: A Survey

The oil and gas industry (OGI) has always been associated with challenges and complexities. It involves many processes and stakeholders, each generating a huge amount of data. Due to the global and distributed nature of the business, processing and managing this information is an arduous task. Many issues such as orchestrating different data sources, owners and formats; verifying, validating and securing data streams as they move along the complex business process pipeline; and getting insights from data for improving business efficiency, scheduling maintenance and preventing theft and fraud are to be addressed. Artificial intelligence (AI), and machine learning (ML) in particular, have gained huge acceptance in many areas recently, including the OGI, to help humans tackle such complex tasks. Furthermore, multi-agent systems (MAS) as a subfield of distributed AI meet the requirement of distributed systems and have been utilised successfully in a vast variety of disciplines. Several studies have explored the use of ML and MAS to increase operational efficiency, manage supply chain and solve various production- and maintenance-related tasks in the OGI. However, ML has only been applied to isolated tasks, and while MAS have yielded good performance in simulated environments, they have not gained the expected popularity among oil and gas companies yet. Further research in the fields is necessary to realise the potential of ML and MAS and encourage their wider acceptance in the OGI. In particular, embedding ML into MAS can bring many benefits for the future development of the industry. This paper aims to summarise the efforts to date of applying ML and MAS to OGI tasks, identify possible reasons for their low and slow uptake and suggest ways to ensure a greater adoption of these technologies in the OGI

Prediction of Capillary Pressure and Relative Permeability Curves using Conventional Pore-scale Displacements and Artificial Neural Networks

Traditional network models use simplified pore geometries to simulate multiphase flow using semi-analytical correlation-based approaches. In this work, we aim at improving these models by (I) extending the numerical methodologies to account for pore geometries with convex polygon cross sections and (II) utilizing Artificial Neural Networks (ANN) to predict flow-related properties. Specifically, we simulate fluid displacement sequences during a drainage process in bundles of capillary tubes with randomly generated convex polygon cross-sections. In the beginning, we assume that capillary tubes are fully saturated with water and that they are strongly water-wet. Then, oil is injected to displace water during the primary drainage process. The model calculates threshold capillary pressures for all randomly generated geometries using Mayer-Stowe-Princen (MS-P) method and the minimization of Helmholtz free energy for every pore-scale displacement event. Knowing pore fluid occupancies, we calculate saturations, phase conductances, and two-phase capillary pressure and relative permeability curves. These parameters are then used as input to train an ANN. ANN theories and related applications have been significantly promoted due to the fast increasing performance of computer hardware and inheratively complicated nature of some research areas. Various Artificial Intelligence (AI) applications have been developed specifically for the oil and gas industry such as AI assisted history matching, oil field production and development predictions, and reservoir characterization. The objective of this study is to develop an ANN training and predicting workflow that can be integrated with the conventional pore network modeling techniques. This hybrid model is computationally much faster which is beneficial for large-scale simulations in 3D. It could also be used to improve prediction of flow-related properties in similar rock types. Specifically, we are interested in the training of ANNs to predict threshold capillary pressures and multi-phase flowrates as a function of cross-sectional shapes and wettabilities given for each capillary tube of the bundle. To do so, we have generated multi-phase flow properties for two large datasets consisting of 40,000 and 60,000 capillary tubes each. The predictive capability of the ANN is gauged by performing some quality control steps including blind test validations. We present the results primarily by demonstrating the calculated errors and deviations for any randomly generated bundles of capillary tubes from the aforementioned dataset. We show that generating high-quality training dataset is critical to improving model’s predictive capabilities for a wide range of pore geometries, e.g., shape factors and elongations. Additionally, we demonstrate that feature selection and preprocessing of the input data could significantly impact ANN’s predictions. We analyze a wide range of structures for the ANN models. The Multi-layer perceptron (MLP) Neural Network with three hidden layers is adequate for dealing with the complexity and non-linearity of most of our studied cases. This model is approximately an order of magnitude faster than conventional direct calculations using a personal desktop computer with four cores CPU. Such improvement in the speed of calculations becomes extremely important when dealing with larger models, adding more dimensionality, and/or introducing pore connectivity in 3D

Numerical and experimental study of the impact of temperature on relative permeability in an oil and water system.

Relative permeability is affected by several flow parameters, predominantly operating temperature and fluid viscosity. Fluid viscosity changes with temperature, which correspondingly affects the relative permeability. Temperature is believed to have a considerable effect on oil–water relative permeability and thus is a vital input parameter in petroleum reservoir development modelling. The actual effect of temperature on oil–water relative permeability curves has been a subject of debate within the scientific community. This is based on contradictory experimental and numerical results concerning the effect of temperature on oil–water relative permeability in literature. This study investigates the effect of temperature on multiphase flow physics in a porous media under varying temperature conditions. A computational fluid dynamics approach was adopted for a pore-scale study of the temperature effect on oil recovery factor under a water- and oil-wet condition. For the oil–water relative permeability investigation, a series of coreflooding experiments were conducted with well-sorted unconsolidated silica sandpacks, adopting the unsteady-state relative permeability method. The series of experiments were performed at different temperatures (range between 40 to 80 °C). Three levels of injection flowrates (0.5, 0.75 and 1.0 mL/min) and two oil viscosities (43 cP motor and 21 cP mineral oil – at 60 oC) were used in the study. A history matching approach using the commercial software Sendra was used to determine the oil-water relative permeability for each respective temperature, flowrate and oil viscosity. A support vector regression algorithm was later implemented for the machine learning modelling aspect of this work, which can predict reliable temperature dependent oil–water relative permeability. The pore-scale results showed that the displacement behaviour of water and oil-wet system is strongly affected by the contact angle with a profound effect on the oil recovery factor. The water-wet system resulted in about 35 – 45 % more oil recovery than the oil-wet system, with the unrecovered oil mainly adhering to the wall region of the pore bodies of the oil–water system. The results from all the experimental cases showed that the oil–water relative permeability is a function of temperature, water injection flowrate and oil viscosity. In addition, the experimental findings show a decreasing residual oil saturation of the more viscous fluid with increasing injection flowrate. The end-point water relative permeability varies slightly for the set of experiments, with the values higher for the less viscous oil under the same flowrate condition. Generally, the profile of oil and water relative permeability curve changes with varying oil viscosity and water injection flowrate at the same operating condition. This behaviour shows that the viscosity of oil is an important factor to be considered when selecting displacement flowrate to guarantee high oil production. Furthermore, an increment in temperature results in a corresponding rise in the relative permeability of both oil and water. Comparison of the experimental and machine learning results showed a good match, with consistency across all datasets. In addition to the machine learning model, this study proposes a modified empirical model using nonlinear least square regression for application in unconsolidated porous media. The output from this model can be applied for relative permeability prediction, preliminary evaluation in experimental design and as a valuable benchmarking tool for future laboratory experiments under varying temperature conditions

Foraminiferal Biostratigraphy and Depositional Environment of the Early Cretaceous Drilled Succession in Durban Basin, East Coast, South Africa

>Magister Scientiae - MScDurban Basin located on the eastern coast of South Africa has been a focus of interest for Petroleum Exploration for the last few decades. Only four exploratory wells have been drilled in this offshore basin without success. During the initial stage of its creation, the basin suffered major tectonic disturbance as evident from the presence extensional faults followed by intense igneous activities. This was followed by marine sedimentation in the late Mesozoic (late Jurassic-early Cretaceous). An attempt has been made in this work to understand the distribution of the rock in space and time for the early Cretaceous sediments considered most prospective for hydrocarbon exploration in Southern Africa. Temporal distribution of planktonic foraminifera helps in identification of the three early Cretaceous (Barremian to Albian) stages within the drilled intervals. Foraminiferal biostratigraphic studies integrated with sedimentology, log motif analysis and seismic data analysis helps to predict paleodepth and depositional environment during early Cretaceous in this research. The integrated analysis reveals that during the Barremian-early Aptian stages graben filled sediments were deposited in a marine shelf in the northern part of the studied area (site Jc-D1) whereas, in the central and southern part finer clastics were deposited in middle slope (site Jc-B1 and Jc-C1). The thick claystone section and presence of minor limestone lenses and their benthic foraminifera assemblage in late Aptian-Albian stage in the northern area indicates possibility of submarine fan. Overlying succession dated between late Aptian to Albian and early part of Cenomanian interval in the three studied exploratory wells shows serrated log signatures. The dominant claystone lithology with intermittent siltstone/sandstone units and the benthic foraminifera indicates fluctuating distal marine slope environment with periodic shallowness in the entire area

New Developments in Renewable Energy

Renewable energy is defined as the energy which naturally occurs, covers a number of sources and technologies at different stages, and is theoretically inexhaustible. Renewable energy sources such as those who are generated from sun or wind are the most readily-available and possible solutions to address the challenge of growing energy demands in the world. Newer and environmentally friendly technologies are able to provide different social and environmental benefits such as employment and decent environment. Renewable energy technologies are crucial contributors to world energy security, reduce reliance on fossil fuels, and provide opportunities for mitigating greenhouse gases. International public opinion indicates that there is strong support for a variety of methods for solving energy supply problems, one of which is utilizing renewable energy sources. In recent years, countries realized that that the renewable energy and its sector are key components for greener economies