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

Using AI and Machine Learning to Indicate Shale Anisotropy and Assist in Completions Design

Operating companies in the unconventional Marcellus shale play have all faced a similar and problematic issue, while attempting to produce natural gas over the last decade. Companies have quickly realized that not every perforation along their horizontal wells are producing gas. In fact, producing perforations are only ranging from 15%-70% of the total perforations along the horizontal wellbore [1]. This unexplained issue results in millions of dollars in lost revenue per well, in addition to the sunk cost of paying for completions that are not actually yielding any produced gas. What is causing these perforations to have no produced gas? There are many theories being researched in the private sector and academia including: stress shadowing, proppant type and concentration, sand-outs, unconventional reservoir modeling, and improved geosteering. While any and all of those situations may have an impact on production, this study will focus on one potential issue with shale wells that may be the root cause of this phenomenon: the anisotropic nature of shale. By nature, shale is highly anisotropic, which means that the physical properties of shale change significantly from point to point in the x, y, and z directions. This is caused by the laminar structure of the shale due to the shales formation, effecting properties in the z-direction, as well as widespread natural fracturing effecting properties in the x-y directions [20]. Is it possible that the random and highly variable physical properties of the Marcellus shale are responsible for poor fracture propagation and production at various perforated clusters along the horizontal wellbore? If the physical properties of the shale change considerably along the wellbore, then an area with favorable geomechanical properties for hydraulic fracturing such as Young’s Modulus and Poisson’s Ratio could quickly become unfavorable conditions simply a few feet away. An Artificial Intelligence and Machine Learning method called Fuzzy Logic C-Means Clustering can be used to identify these random changes in shale properties along the wellbore. This is done by gathering raw measured data from sources such as a sonic log or natural fracture log and allowing the AI algorithm to classify each half-foot of shales along the wellbore into groups of ‘like’ shales. These newly defined classifications of shales are grouped together to include shales with similar physical properties to each other. This can be used to identify areas of anisotropy along the wellbore that would have previously been unseen, allowing for an engineered completions design that ensures all perforated clusters will be placed against shales with similar physical properties. This is likely to result in improved overall production within a stage, since fractures would not be induced at different types or qualities of shales within a single stage. The theory is that a stage where every cluster successfully propagates a fracture will have higher production than a stage with one or two dominant fractures. The individual fractures may be smaller using this method, but the improved cluster efficiency could see improved production. The use of C-Means Fuzzy Clustering is validated when clustering sonic and natural fracture log data for the MSEEL well MIP-3H, and comparing the changes in classification with a production log for the well. The changes in classification are quantified as an anisotropy indicator value (AIV). When comparing the AIV with the production, a peak and valley relationship is observed. When the AIV is high, the production is low or near zero at that given cluster. When the AIV is low, the opposite is true. In fact, the Fuzzy C-Means Clustering model was able to identify a high AIV at 88% of the non-producing clusters for MIP-3H. This suggests a strong correlation between the anisotropy of shale, and its effect on achieving a successful completions design. The Fuzzy C-Means model can then be applied to a full horizontal wellbore sonic and natural fracture log in order to optimize a more successful completions design that is likely to see improved cluster efficiency when accounting for the shale anisotropy

Near real-time classification of iron ore lithology by applying fuzzy inference systems to petrophysical downhole data

Fluctuating commodity prices have repeatedly put the mining industry under pressure to increase productiveness and efficiency of their operations. Current procedures often rely heavily on manual analysis and interpretation although new technologies and analytical procedures are available to automate workflows. Grade control is one such issue where the laboratory assay turn-around times cannot beat the shovel. We propose that for iron ore deposits in the Pilbara geophysical downhole logging may provide the necessary and sufficient information about rock formation properties, circumventing any need for real-time elemental analysis entirely. This study provides an example where petrophysical downhole data is automatically classified using a neuro-adaptive learning algorithm to differentiate between different rock types of iron ore deposits and for grade estimation. We exploit a rarely used ability in a spectral gamma-gamma density tool to gather both density and iron content with a single geophysical measurement. This inaccurate data is then put into a neural fuzzy inference system to classify the rock into different grades and waste lithologies, with success rates nearly equal to those from laboratory geochemistry. The steps outlined in this study may be used to produce a workflow for current logging tools and future logging-while-drilling technologies for real-time iron ore grade estimation and lithological classification

Developing a method for identification of net zones using log data and diffusivity equation

International audienceDistinguishing productive zones of a drilled oil well plays a very important role for petroleum engineers to decide where to perforate to produce oil. Conventionally, net pay zones are determined by applying a set of cutoffs on perophysical logs. As a result, the conventional method finds productive intervals crisply. In this investigation, a net index value is proposed, then; diffusivity equation is utilized to calculate the proposed index value. The new net determination method is applied on the interval of Sarvak Formation of two datasets of two nearby wells. The best advantage of this newly developed net determination method is its fuzzy output. Fuzzy net pay determination is valuable in grading pay zones and not classifying all productive zones in a single class. Another advantage of the proposed net determination method is its higher accuracy in identifying productive zones in comparison with cutoff based method

Synthetic Well Log Generation Software

In this study, we developed a novel approach to generate synthetic well logs using backpropagation neural networks through the use of an open source software development tool. Our method predicts essential well logs such as neutron porosity, sonic, photoelectric, and resistivity, which are crucial in various stages of oil and gas exploration and development, as they help determine reservoir characteristics. Our approach involves sequentially predicting well logs, using the outputs of one prediction model as inputs for subsequent models to generate comprehensive and coherent sets of well logs. We trained and tested our models using 16 wells from a single field, and the resulting synthetic well logs demonstrated an acceptable degree of accuracy and consistency with the actual logs, thus supporting the efficacy of our approach. This research not only opens up new avenues for enhancing the efficiency of hydrocarbon exploration but also contributes to the growing body of knowledge in the field of AI and ML applications in the oil and gas industry. This work also demonstrates the capabilities of open source tools for developing software and for oil and gas applications

Unconventional Reservoir Characterization and Formation Evaluation: A Case Study of a Tight Sandstone Reservoir in West Africa

Unconventional reservoirs, including gas shales and tight gas sands, have gained prominence in the energy sector due to technological advancements and escalating energy demands. The oil industry is eagerly refining techniques to decipher these reservoirs, aiming to reduce data collection costs and uncertainties in reserve estimations. Characteristically, tight reservoirs exhibit low matrix porosity and ultra-low permeability, necessitating artificial stimulation for enhanced production. The efficacy of the stimulation hinges on the organic material distribution, the rock’s mechanical attributes, and the prevailing stress field. Comprehensive petrophysical analysis, integrating standard and specialized logs, core analyses, and dynamic data, is pivotal for a nuanced understanding of these reservoirs. This ensures a reduction in prediction uncertainties, with parameters like shale volume, porosity, and permeability being vital. This article delves into an intricate petrophysical evaluation of the Nene field, a West African unconventional reservoir. It underscores the geological intricacies of the field, the pivotal role of data acquisition, and introduces avant-garde methodologies for depth matching, rock typing, and the estimation of permeability. This research highlights the significance of unconventional reservoir exploration in today’s energy milieu, offering a granular understanding of the Nene field’s geological challenges and proffering a blueprint for analogous future endeavours in unconventional reservoirs

Machine Learning Assisted Framework for Advanced Subsurface Fracture Mapping and Well Interference Quantification

The oil and gas industry has historically spent significant amount of capital to acquire large volumes of analog and digital data often left unused due to lack of digital awareness. It has instead relied on individual expertise and numerical modelling for reservoir development, characterization, and simulation, which is extremely time consuming and expensive and inevitably invites significant human bias and error into the equation. One of the major questions that has significant impact in unconventional reservoir development (e.g., completion design, production, and well spacing optimization), CO2 sequestration in geological formations (e.g., well and reservoir integrity), and engineered geothermal systems (e.g., maximizing the fluid flow and capacity of the wells) is to be able to quantify and map the subsurface natural fracture systems. This needs to be done both locally, i.e., near the wellbore and generally in the scale of the wellpad, or region. In this study, the conventional near wellbore natural fracture mapping techniques is first discussed and integrated with more advanced technologies such as application of fiber optics, specifically Distributed Acoustic Sensing (DAS) and Distributed Strain Sensing (DSS), to upscale the fracture mapping in the region. Next, a physics-based automated machine learning (AutoML) workflow is developed that incorporates the advanced data acquisition system that collects high-resolution drilling acceleration data to infer the near well bore natural fracture intensities. The new AutoML workflow aims to minimize human bias and accelerate the near wellbore natural fracture mapping in real time. The new AutoML workflow shows great promise by reducing the fracture mapping time and cost by 10-fold and producing more accurate, robust, reproducible, and measurable results. Finally, to completely remove human intervention and consequently accelerate the process of fracture mapping while drilling, the application of computer vision and deep learning techniques in new workflows to automate the process of identifying natural fractures and other lithological features using borehole image logs were integrated. Different structures and workflows have been tested and two specific workflows are designed for this purpose. In the first workflow, the fracture footprints on actual acoustic image logs (i.e., full, or partial sigmoidal signatures with a range of amplitude and vertical and horizontal displacement) is detected and classified in different categories with varying success. The second workflow implements the actual amplitude values recorded by the borehole image log and the binary representation of the produced images to detect and quantify the major fractures and beddings. The first workflow is more detailed and capable of identifying different classes of fractures albeit computationally more expensive. The second workflow is faster in detecting the major fractures and beddings. In conclusion, regional subsurface natural fracture mapping technique using an integration of conventional logging, microseismic, and fiber optic data is presented. A new AutoML workflow designed and tested in a Marcellus Shale gas reservoir was used to predict near wellbore fracture intensities using high frequency drilling acceleration data. Two integrated workflows were designed and validated using 3 wells in Marcellus Shale to extract natural fractures from acoustic image logs and amplitude recordings obtained during logging while drilling. The new workflows have: i) minimized human bias in different aspects of fracture mapping from image log analysis to machine learning model selection and hyper parameter optimization; ii) generated and quantified more accurate fracture predictions using different score matrices; iii) decreased the time and cost of the fracture interpretation by tenfold, and iv) presented more robust and reproducible results

Integration of Geochemical and Geophysical Data for Downhole Rock Mass Characterisation

Exploration for mineral deposits is more challenging today as the target depth for new discoveries is increasing. New measuring and sensing technologies that enable real-time data acquisition are being developed to overcome these challenges. This study investigates how a joint, real-time analysis of these data-streams can add knowledge about the characteristics of a mineral deposit and complement an effective exploration strategy in the future