well logging

ABSTRACT

Nuclear magnetic resonance wireline logging and data post-processing technologies are continuously evolving, making significant contributions to rock, fluid typing, formation evaluation and characterization of the near-wellbore zone. In heavy oil fields, however, nuclear magnetic resonance (NMR) logging is known to provide an underestimated permeability, poor reliable oil typing and thus poor oil saturation and viscosity determinations, especially when the evaluation is based only on the spectra of transverse magnetic relaxation times (T2) (one-dimension NMR) [Romero et al., 2009]. Several attempts have been made to improve NMR results, mostly with limited success [Fang et al., 2004], especially in separating the oil component from the contribution of other fluids to the T2 spectra. The main reason lies not necessarily in the selection of the data acquisition parameters and sequences for a single-frequency or multi-frequency tool, but in the way how the data is post-processed.

The present study refers to a well drilled through the Derdere formation, a limestone/dolomite heavy oil reservoir in Turkey. The NMR data was acquired in with a centralized, single-frequency wireline tool in a 6-in. borehole, drilled with water-based mud in a freshwater carbonate reservoir. The generated T2 log was analyzed in a traditional way to obtain the NMR total porosity and its partitions based on standard cutoff values. For the given 12 API oil gravity, reservoir temperature (76 °C) and gas-oil-ratio (GOR) the T2Oil peak appears around 170 ms, right from the T2 cutoff for limestones; therefore, no corrections were needed on the permeability calculated from the Timur-Coates and Schlumberger-Doll-Research (SDR) equations. In the present well, only a diffused separation between oil and free water could be observed on the T2 distribution log from field data.

In the broader concept of Artificial Intelligence, the newly proposed post-processing steps to obtain the oil saturation start by deconvolving the T2 spectra, using blind source separation (BSS) based on independent component analysis (ICA) [Romero, 2016; Romero Rojas et al., 2018]. Based on its T2 peak value —the expected T2Oil peak response— calculated from the prejob planner/simulator, the deconvolution results show that one specific independent component corresponds to the oil, from which the oil saturation was determined.

Results demonstrated the usefulness of NMR logging technology in the characterization and evaluation of this reservoir. Data post-processing based on BBS-ICA enable adequate differentiation between fluid components from T2 spectra. For the reasons above, NMR has been proposed for additional wells in the same field.

ABSTRACT

In the past, much of the petrophysics done in the Australian mining industry has been based upon gamma ray, simple density devices, resistivity, and televiewers. Common uses of petrophysical data include locating the top and bottom of the seam/ore, determining the water level, mapping fractures and faults, computing hardness, and facies analysis. However, the industry is moving toward more advanced applications, such as improved methods of understanding the porosity and permeability of the rocks, 3D mapping of stability, and the use of petrophysical measurements as a cost-effective means of supplementing or even replacing traditional assay methods.

This paper begins with a brief introduction to the mining environment as compared with the modern oilfield environment. While petrophysical data acquisition in East Australian coal mines is not so far removed from shallow oilfield land wells, open pit mines, such as the Pilbara Iron Ore fields of Western Australia are a very different world - thousands of holes are drilled, each generally less than 60 metres. Assays (geological analysis of material collected from the hole) are the primary reference data. Costs to log are low and many processes (data interpretation, delivery of logs, etc.) are automated.

Next we will review how gamma ray, density, neutron, resistivity, and caliper measurements are used throughout the Australian mining industry, paying some attention to the challenges of using classic tool designs such as 16/64 normal resistivity tools and single point (uncompensated) density. Sonic, electrical imaging, and optical televiewers are the next tier of measurements, used for fracture/fault mapping, ground stability, hardness and seismic integration. Finally, we will discuss the latest wave of technologies to be gaining ground in the Australian mining market, including NMR, VSP, and elemental spectroscopy.

The introduction of advanced petrophysical measurements in Australian mining is opening the door for exploiting new applications, many centered around “big data” or machine learning techniques, such as automated facies identification, high resolution mapping of both major and minor minerals, and 3D visualisation of ore properties.

ABSTRACT

The new-generation oil-base mud (OBM) microresistivity imagers provide photorealistic high-resolution quantified formation imaging. One of the existing interpretation methods is based on composite processing providing an apparent resistivity image largely free of the standoff effect. Another one is the inversion-based workflow, which is an alternative quantitative interpretation, providing a higher quality resistivity image, button standoff, and formation permittivities at two frequencies. In this work, a workflow based on artificial neural networks (NNs) is developed for quantitative interpretation of OBM imager data as an alternative to inversion-based workflow.

The machine learning approach aims to achieve at least the inversion-level quality in formation resistivity, permittivity, and standoff images an order of magnitude faster, making it suitable for implementation on automated interpretation services as well as integration with other machine learning based algorithms. The major challenge is the underdetermined problem since OBM imager provides only four measurements per button, and eight model parameters related to formation, mud properties, and standoff need to be predicted. The corresponding nonlinear regression problem was extensively studied to determine tool sensitivities and the combination of inputs required to predict each unknown parameter most accurately and robustly. This study led to the design of cascaded feed-forward neural networks, where one or more model parameters are predicted at each stage and then passed on to following steps in the workflow as inputs until all unknowns are accurately obtained.

Both inverted field data sets and synthetic data from finite-element electromagnetic modeling were used in multiple training scenarios. In the first strategy, field data from few buttons and existing inversion results were used to train a single NN to reproduce standoff and resistivity images for all other buttons. Although the generated images are comparable to images coming from inversion, the method is dependent on the availability of field data for variable mud properties, which at the moment limits the generalization of the NNs to diverse mud and formation properties.

In the second strategy, we utilized the synthetic responses from a finite element model (FEM) simulator for a wide range of standoffs, formation, and mud properties to develop a cascaded workflow, where each stage predicts one or more model parameters. Early stages of the workflow predict the mud properties from low formation resistivity data sections. NNs then feed the estimated mud angle and permittivities at two frequencies into next stages of the workflow to finally predict standoff, formation resistivity, and formation permittivities. Knowledge of measurement sensitivities was critical to design the efficient parameterization and robust cascaded neural networks not only due mathematically underdetermined nature of the problem but also the wide dynamic range of mud and formation properties variation and the measurements. Results for processed resistivity, standoff, and permittivity images are presented, demonstrating very good agreement and consistency with inversion-generated images. The combination of two strategies, training on both synthetic and field data, can lead to further improvement of robustness allowing customization of interpretation applications for specific formations, muds, or applications.

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.

The initial high cost of exploitation of the sustained, increasingly growing development of unconventional resources in Argentina has resulted in concentrating all efforts to increase well productivity while reducing construction and completion costs. The optimization of hydraulic fracture (HF) treatments is vitally important. It is the primary strategy used to achieve an optimal reservoir drainage area, consequently characterizing the fracture geometry, including the height, for the continuous improvement of HF treatment and planning.

Several types of technologies and methodologies are used to estimate fracture height during and after a hydraulic stimulation treatment. These technologies can provide information about the fracture geometry and extension in the near-wellbore (NWB) and far-field areas. The determination of a reliable correlation between those methodologies represents a challenge as a result of formation complexity, heterogeneity, and limitations of evaluation technologies. It is well-known that some areas in the Vaca Muerta formation contain layers that can act as fracture barriers and are responsible for fracture containment.

This paper presents a fast and simple methodology that uses conventional well logs [gamma ray (GR), sonic, and density] from pilot wells to identify potential fracture barriers. This approach establishes a means to evaluate the degree to which the rock will have the ability to control fracture height growth. This methodology was determined useful for planning perforation intervals or clusters placement, particularly in those formations with stress profile showing reduced stress contrast and, when complemented with geological information, this method also provides useful information for horizontal well trajectory. Case studies are provided to illustrate examples of the proposed fracture barrier index (FBI) being calibrated or compared to other fracture height assessment. Additionally, the benefits of adding this new approach to current methodologies and technologies to aid completion design optimization and decision making is discussed.

Mud gas data from drilling operations provide the very first indication of the presence of hydrocarbons in the reservoir. It has been a dream for decades in the oil industry to predict reservoir gas and oil properties from mud gas data, because it would provide knowledge of the reservoir fluid properties in an early stage, continuously for all reservoir zones, and at low costs. Previous efforts reported in the literature did not lead to a reliable method for quantitative prediction of the reservoir fluid properties from mud gas data. In this paper, we propose a novel approach based on machine learning which enables us to predict gas oil ratio (GOR) from advanced mud gas (AMG) data.

The current work is based on a previous successful pilot in unconventional (shale) reservoirs. Our aim is to extend the results of the pilot study to conventional reservoirs. In general, prediction of reservoir fluid properties is more challenging for conventional reservoirs than for unconventional reservoirs, due to the complexity of petroleum systems in conventional reservoirs. Instead of building a model directly from AMG data, we trained a machine learning model using a well-established reservoir fluid database with more than 2000 PVT samples. After thorough investigation of compositional similarity between PVT samples and AMG data, we applied the model developed from PVT samples to AMG data.

The predicted GORs from AMG data were compared with GOR measurements from corresponding PVT samples to assess the accuracy of the GOR predictions. The results from 22 wells with both AMG data and corresponding PVT samples show large agreement between prediction vs. measurement. The accuracy of the predictive model is much higher than previous results reported in the literature. In addition, a Quality Check (QC) metric was developed to efficiently flag low-quality AMG data. The QC metric is vital to give confidence level for GOR prediction based on AMG data when PVT samples are not available.

The study confirms that AMG data can be used as a new data source to quantitatively predict continuous reservoir fluid properties in the drilling phase. The method can be used to optimize wireline operations and for some cases, it provides a unique opportunity to acquire reservoir fluid data when conventional fluid sampling or use of wireline tools is not possible. After high-quality PVT data becomes available in the wireline logging phase, the continuous GOR prediction can be further improved and used to determine reservoir fluid gradient and reservoir compartmentalization.

A high risk of suboptimal well placement exists in new field development where seismic uncertainty can be great. Recent ultradeep resistivity measurement developments provide great benefits for identifying and optimizing the well path position within a given stratigraphic sequence. This paper presents a case study in which an operator planned to place wells 10 m TVD below the reservoir top because of seismic uncertainty of the top reservoir pick. To help mitigate this subsurface risk, the field development plan required real-time well placement optimization, using both standard formation evaluation data and an ultradeep azimuthal resistivity service. In this case-history, the ultradeep inversion canvases could be used to identify the well path position within the reservoir, as well as provide sufficient confidence to steer the well closer to the reservoir top than originally planned.

Multiple geological models, created from nearby offset wells and seismic grids, represented the expected seismic uncertainty of 5 to 15 m TVD. To identify the optimal measurement setup for real-time operations, resistivity modelling illustrated the effect of frequency and spacing on the data, producing multiple inversions for each geological scenario. After drilling began, real-time inversions for the ultradeep resistivity data were initially qualified using standard formation evaluation data, including both deep azimuthal resistivity and azimuthal density images. Multiple inversion canvases from various spacings and frequencies identified several formation features, including distances to the top and base of the reservoir. The quantified uncertainty of these results assisted in the evaluation of the inversion quality.

When close to the reservoir top, the wellbore position indicated in the ultradeep inversion canvases matched the interpretation from the conventional logs, which provided increased confidence in the inversion canvas results at distances farther away. This enhanced reservoir knowledge enabled the operator to progressively raise the well path to 5 and to 2 m TVD from the reservoir top. Except for strategic geosteering decisions based on expected faults positions from the seismic data, the operator made most well-placement decisions, across multiple wells, using ultradeep resistivity data. The high data quality and close collaboration within the subsurface team quickly led to high confidence in the inversion results. Integrating the full suite of available data, from shallow to ultradeep measurements in a comprehensive interpretation, provided better reservoir understanding, resulting in optimal well placement.

This paper presents formation evaluation results used within an integrated well-placement optimization service from a new field development. The integrated data qualified the results for an ultradeep resistivity tool. Confidence in the tool results enabled the operator to place wells much closer to the reservoir top than initially planned, in an area of seismic uncertainty.

Cost-effective exploitation of heterogeneous/anisotropic reservoirs (e.g., carbonate formations) reckons on accurate description of pore structure, dynamic petrophysical properties (e.g., directional permeability, saturation-dependent capillary pressure), and fluid distribution. However, techniques for reliable quantification of permeability and hydrocarbon saturation still rely on model calibration using core measurements. Furthermore, assessment of saturation-dependent capillary pressure has been limited to experimental measurements, such as mercury injection capillary pressure (MICP). The objectives of this paper include (a) developing a new multiphysics workflow to simultaneously quantify rock fabric features (e.g., porosity, tortuosity, and effective throat size) and hydrocarbon saturation from integrated interpretation of nuclear magnetic resonance (NMR) and electric measurements, (b) introducing rock physics models that incorporate the quantified rock fabric and partial water/hydrocarbon saturation for assessment of directional permeability and saturation-dependent capillary pressure, and (c) validating the reliability of the new workflow in pore- and core-scale domains.

To achieve these objectives, we introduce a new multiphysics workflow integrating NMR and electric measurements, honoring rock fabric, and minimizing calibration efforts. We estimate water saturation from the interpretation of dielectric measurements. Next, we develop a fluid substitution algorithm to estimate the T₂ distribution corresponding to fully water-saturated rocks from the interpretation of NMR measurements. We use the estimated T₂-distribution for assessment of porosity, pore-size distribution, and effective pore-body size. Then, we develop a new physically meaningful resistivity model and apply it to obtain the constriction factor and, consequently, throat-size distribution from interpretation of resistivity measurements. Finally, throat-size distribution, porosity, and tortuosity are used to calculate directional permeability and saturation-dependent capillary pressure. We test the reliability of the new multiphysics workflow in core- and pore-scale domains on rock samples at different water saturation levels.

The introduced multiphysics workflow provides accurate description of the pore structure and fluid distribution in partially water-saturated formations with complex pore structure. Moreover, this new method enables real-time well-log-based assessment of saturation-dependent capillary pressure and directional permeability (in presence of directional electrical measurements) in reservoir conditions, which was not possible before. Quantification of capillary pressure has been limited to measurements in laboratory conditions, where the differences in stress field reduce the accuracy of the estimates. We verified that the estimates of permeability, saturation-dependent capillary pressure, and throat-size distribution obtained from the application of the new workflow agreed with those experimentally determined from core samples. Finally, since the new workflow relies on fundamental rock physics principles, hydrocarbon saturation, permeability, and saturation-dependent capillary pressure can be estimated from well-logs with minimum calibration efforts, which is another unique contribution of this work.

Formulating a prediction tool that can estimate the formation permeability in uncored wells is of particular importance for many applications related to reservoir simulation and production management. Although formation permeability can be obtained from a laboratory or from a reservoir, core analysis and well-test data are limited due to cost and time-saving purposes. A major challenge of previous methods is that they are required other parameters to be previously computed such as porosity and water saturation. In addition, they are affected by the uncertainty that introduced by the cementation factor and saturation exponent. This study presents two prediction methods, multiple regression analysis (MRA) and artificial neural networks (ANNs), to estimate formation permeability using conventional well log data.

The prediction methods were demonstrated by means of a field case in SE Iraq. The study uses core/well log data from Mishrif reservoir which is mainly composed of carbonate (limestone) formations. Two traditional methods were reviewed and presented for permeability determination. These methods are the classical method and the flow zone indicator (FZI) method.

At the same porosity, the results showed a wide range of formation permeability prediction. This result gives a special attention to the assumption that the relationship between permeability and porosity is generally unique in carbonate environments. The deep lateral log resistivity appears to be more conservative in the permeability function rather than other parameters, followed in decreasing order by bulk density, sonic travel time, micro and shallow resistivities, and shale volume. Although the presented models based on RA and ANNs resemble to be closely in determining the formation permeability, the correlation coefficient of ANNs was found to be higher than that obtained from RA, which indicated that the ANNs is more precise than RA. The comparison among previous methods shows the superiority of the FZI method rather than the classical method. However, core porosity and permeability should be previously determined to apply FZI method. This study presents efficient and cost-effective models for a prediction of permeability in uncored wells by incorporating conventional well logs.

This paper compares the results of gas identification and lithology identification using pulsed-neutron spectroscopy in openhole and casedhole environments. Coring is essential to offshore exploration programs—but sometimes cores are taken from the wrong formation or return to surface in poor condition. One firm thinks it can solve these costly issues with a first-of-a-kind coring device that uses logging instruments that add accuracy and integrity. The rising cost of fracturing offers a significant incentive for finding ways to avoid unproductive rock. One entrepreneur says he can use standard well logs to target the slice of rock likely produce most, and avoid the rest.