A review of basic well log interpretation techniques in highly deviated wells

Drilling deviated wells has become customary in recent times. This work condenses various highly deviated and horizontal well log interpretation techniques supported by field examples. Compared to that in vertical wells, log interpretation in highly deviated wells is complex because the readings are affected not only by the host bed but also the adjacent beds and additional wellbore-related issues. However, understanding the potential pitfalls and combining information from multiple logs can address some of the challenges. For example, a non-azimuthally focused gamma ray logging while drilling (LWD) tool, used in combination with azimuthally focused density and neutron porosity tools, can accurately tell if an adjacent approaching bed is overlying or underlying. Moreover, resistivity logs in horizontal wells are effective in detecting the presence of adjacent beds. Although the horns associated with resistivity measurements in highly deviated wells are unwanted, their sizes can provide important clues about the angle of the borehole with respect to the intersecting beds. Inversion of horizontal/deviated well logs can also help determine true formation resistivities. Additionally, observed disagreement between resistivity readings with nuclear magnetic resonance (NMR) T2 hydrocarbon peaks can indicate the presence or absence of hydrocarbons. Furthermore, variations in pulsed neutron capture cross sections along horizontal wells, measured while injecting various fluids, can indicate high porosity/permeability unperforated productive zones. Finally, great advances have been made in the direction of the bed geometry determination and geologic modeling using the mentioned deviated well logs. More attention is required toward quantitative log interpretation in horizontal/high angle wells for determining the amount of hydrocarbons in place.

Matrix Dielectric Permittivity for Enhanced Formation Evaluation

Modern dielectric tools are often run to obtain fundamental formation properties, such as remaining oil saturation, water-filled porosity, and brine salinity. Techniques to extract more challenging reservoir petrophysical properties like Archie m and n parameters are also emerging. The accuracy and representativeness of the obtained petrophysical parameters depend on the input parameter accuracy, such as matrix permittivity. In carbonates, matrix permittivity is known to vary over a wide range, for example, limestone matrix permittivity reported in the literature ranges from 7.5 to 9.2. The main objective of the current study is to reduce matrix dielectric permittivity uncertainty for enhanced formation evaluation in carbonate reservoirs. All dielectric measurements were conducted on 1.5 in. carbonate plug samples by means of a coaxial reflection probe with a range of frequency between 10 MHz and 1 GHz. To calculate matrix mineral dielectric permittivity, sample porosity must be obtained. Stress-corrected helium porosity from routine core analysis is used and samples mineralogy and chemical composition are measured by X-Ray diffraction. Dielectric system calibration is done by utilizing several well-characterized standards with known dielectric properties. Calcite and dolomite matrix permittivity are assessed by laboratory measurements. Results of this study and based on data from 180 core plugs allowed to assess the validity of the defined errors by statistical analysis, resulting in much reduced uncertainties in carbonate rock matrix dielectric permittivity; thus enhancing formation evaluation using dielectric measurements. The current study provides better control on dielectric permittivity values used in dielectric log interpretation for limestone formations. Such knowledge will provide better confidence in interpreted data such as water-filled porosity, flushed zone salinity and water phase tortuosity.

From Completion Design to Efficiency Analysis of Inflow Control Device: Comprehensive Approach for AICD Implementation for Thin Oil Rim Field Development Efficiency Improvement

The complex interbedded heterogeneous reservoirs of the Severo-Komsomolskoye field are developed by horizontal wells in which, as part of the pilot project's scope, autonomous inflow control devices (AICD) are installed to prevent early coning and gas breakthroughs in long horizontal sections and reduce sand production, which is a problem aggravated by an extremely low mechanical strength of the terrigenous deposits occurring in the Pokur formation of the Cenomanian stage in this area. The zones produced through AICDs are separated by swell packers. The issue of AICD effectiveness is discussed in the publications by Solovyev (2019), Shestov (2015), Byakov (2019) and some others. One of the methods used for monitoring horizontal sections with AICDs is production logging (PLT). However, due to the complexity of logging objectives, the use of conventional logging techniques makes the PLT unfeasible, considering the costs of preparing and carrying out the downhole operations. This paper provides some case studies of the Through-Barrier Diagnostics application, including passive spectral acoustics (spectral acoustic logging) and thermohydrodynamic modelling for the purpose of effective estimation of reservoir flows behind the liner with AICDs installed and well integrity diagnostics. As a result of the performed diagnostics, the well completion strategy was updated and optimised according to the log interpretation results, and one well intervention involving a cement squeeze with a straddle-packer assembly was carried out.

Automating the Log Interpretation Workflow Using Machine Learning

Log interpretation is the task of analyzing and processing well logs to generate the subsurface properties around wells. A direct application of machine learning (ML) to this task is to train an ML model for predicting properties in target wells given well logs (data) and properties (labels) in a set of training wells in the same field and/or region. Our ML model of choice for predicting the desired properties is the decision tree-based learning algorithm called random forests (RF). We also devise a mechanism to automatically tune the hyperparameters of this algorithm depending on the data in the training wells. This eliminates the tedious task of carefully tuning the hyperparameters for every new set of training wells and provides a one-click solution. In addition to predicting the properties, we compute the uncertainty in the predicted properties in the form of prediction intervals using the concept of quantile regression forests (QRF). We test our workflow on two use cases. First, we consider a petrophysics use case on an unconventional land dataset to predict the petrophysical properties such as water saturation, total porosity, volume of clay, and total organic carbon from petrophysics logs. Then, we consider a geomechanics use case on a conventional offshore dataset to predict the lithology, pore pressure, and rock mechanical properties. We obtain a good prediction performance on both use cases. The uncertainty estimates also complement the ML model's prediction of the properties by explaining the various correlations that are found to be existing among them based on domain knowledge. The entire workflow of automating the tuning of hyperparameters and training the ML model to predict the properties along with its estimate of uncertainty provide a complete solution to apply the ML workflow for automated log interpretation.

Production Logging, Openhole-Log Interpretation Help Discover New Oil Reserves

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 30644, “Discovery of New Oil Reserves by Combining Production Logging With Openhole-Log Interpretation in Low-Resistivity Pay,” by Xinlei Shi, Peichun Wang, and Jinxiu Xu, CNOOC, et al., prepared for the 2020 Offshore Technology Conference, originally scheduled to be held in Houston, 4–7 May. The paper has not been peer reviewed. Copyright 2020 Offshore Technology Conference. Reproduced by permission. In this paper, the authors examine the evaluation of a low-resistivity-pay siliciclastic reservoir in Bohai Bay, China. A significant amount of irreducible water is bound to the rock surface, dramatically lowering the resistivity of the pay zone. The authors explore a theory that the low resistivity is caused by bound water trapped in clay minerals, using production logging to provide the ground truth of reservoir fluids in the low-resistivity pay and improve the petrophysics model. With the improved model, production predictions were made for offset wells based on their openhole logs. The production histories of these wells are highly consistent with the authors’ predictions. Introduction LD oil field is in the eastern Bohai Sea, China, structurally in the transition zone between the Liaohe depression of the Tanlu fault and the Bozhong depression and at the dip end of the Bodong low uplift extending to the northeast. The main oil reservoirs are developed in the Guantao and Dongying formations. Reservoir depth ranges from approximately 1022.1 to 2585.8 m. Reservoir lithology is mainly sandstone and gravelly sandstone. The porosity distribution range of the Guantao formation is 24 to 30%. Permeability distribution range is 333 to 3333 md belonging to medium-high-porosity and - permeability reservoirs. The porosity distribution range of the Dongying formation is from 6 to 12%, and the permeability distribution range is from 3 to 33 md belonging to medium-low- porosity and -permeability reservoirs.

Elastic-Electrical Rock-Physics Template for the Characterization of Tight-Oil Reservoir Rocks

Tight-oil reservoirs have low porosity and permeability, with microcracks, high clay content, and a complex structure resulting in strong heterogeneities and poor connectivity. Thus, it is a challenge to characterize this type of reservoir with a single geophysical methodology. We propose a dual-porosity-clay parallel network to establish an electrical model and the Hashin-Shtrikman and differential effective medium equations to model the elastic properties. Using these two models, we compute the rock properties as a function of saturation, clay content, and total and microcrack porosities. Moreover, a 3D elastic-electrical template, based on resistivity, acoustic impedance, and Poisson’s ratio, is built. Well-log data is used to calibrate the template. We collect rock samples and log data (from two wells) from the Songliao Basin (China) and analyze their microstructures by scanning electron microscopy. Then, we study the effects of porosity and clay content on the elastic and electrical properties and obtain a good agreement between the predictions, log interpretation, and actual production reports.

Quantitative measurement on coal components through the interpretation model of geophysical log: A case study from the Qaidam Basin, NW China

The Iqe coalfield is one of the most significant coal production bases in the Qaidam Basin. Over the last few decades, core explorations have targeted the Dameigou Formation for No. 7 coal seam (M7). Although many M7 coal samples have been analyzed for coal components in the laboratory, the systematic understanding of the components and changes of coal in the whole Iqe coalfield is still inadequate. In this study, we focus on building log interpretation models to accurately calculate the content of coal components of M7, including ash yield (Aad), volatile matter (Vdaf), fixed carbon (FCad), and moisture (Mad). Multiple regression analysis and statistical method, combined with the rock volume model, were used to establish log interpretation models of coal components. A total of 28 coal samples from ZK1, ZK2, ZK11-5, ZK23-4, and ZK36-9 wells in the Iqe coalfield were involved in the modeling, as well as well-logs parameters, such as radioactivity (GR), compensation density (DEN), acoustic (AC), and resistivity (RLLD). According to sensitivity analysis, the fitted Aad and Vdaf contents of M7 increase with the increasing of DEN and GR values, whereas the FCad content shows the opposite way. Furthermore, the positive relationship between Aad and Vdaf ( R2 = 0.59) and the negative relationship between Aad and FCad ( R2 = 0.92) as well as Vdaf and FCad ( R2 = 0.69) indicate that Aad is a key factor in coal and should be prior determined. Finally, based on the multiple regression analysis and rock volume model, we proposed log interpretation models for M7 coal components in the Iqe coalfield, these models have been examined successfully by the case studies from the same coalfield and will provide new insights into the application of geophysical log parameters for coal quality evaluation.

The Pressure Dependence of the Archie Cementation Exponent for Samples from the Ordovician Goldwyer Shale Formation in Australia

Summary Applying the realistic cementation exponent (m) in Archie’s equation is critical for reliable fluid-saturation calculation from well logs in shale formations. In this study, the cementation exponent was determined under different confining pressures using a high-salinity brine to suppress the surface conductivity related to the cation-exchange capacity of clay particles. A total of five Ordovician shale samples from the Canning Basin, Australia, were used for this study. The shale samples are all illite-rich with up to 60% clay content. Resistivity and porosity measurements were performed under a series of confining pressures (from 500 to 8,500 psi). Nuclear magnetic resonance (NMR) was used to obtain porosity and pore-size distribution and to detect the presence of residual oil. The complex impedance of samples was determined at 1 kHz to verify the change in pore-size distribution using the POLARIS model (Revil and Florsch 2010). The variation of shale resistivity and the Archie exponent m at different pressures is caused by the closure of microfractures at 500 psi, the narrowing of mesopores/macropores between 500 and 3,500 psi, and the pore-throat reduction beyond 3,500 psi. This study indicates that unlike typical reservoirs, the Archie exponent m for shale is sensitive to depth of burial because of the soft nature of the shale pore system. An equation is developed to predict m under different pressures after microfracture closure. Our study provides recommended experimental procedures for the calculation of the Archie exponent m for shales, leading to improved accuracy for well-log interpretation within shale formations when using Archie-basedequations.

Geological feature clarification of the contact zone of Akhskaya and Cherkashinskaya formations on the example of Priobskaya area

The first phase of subcycle AC12 formation on the example of the southern part of the Priobskoye field is considered. The job is done based on 3d seismic and geological model complexing with the using log interpretation. The erosion zone that violates the original structure of the upper part of the deep-sea clay pack III of akhskaya formation by paleochannels was revealed. These erosions form lithologic traps, isolated from the top productive reservoirs of subcycle AC12.