363 research outputs found

    Identification of Regional Shale Gas Sweet Spots and Unconventional Reservoirs Using Well Logs and Seismic Data

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    To enable the oil and gas industry conduct exploration and identify potential shale gas sweet spots in a fast and cost-effective manner, the study developed a novel method for predicting in situ rock elastic properties. Specifically, taking the Canning Basin as a case study, correlation equations between static and dynamic rock elastic properties were developed. Such correlation equations can be used to accurately predict the actual in situ rock elastic properties, including the brittleness index

    Advances in multiscale rock physics for unconventional reservoirs

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    The multiscale rock physics of unconventional reservoirs have drawn increasing attention in recent years, which involves several essential issues, including measuring method, transport property, physics model, characteristic scale, and their application. These issues vastly affect science and engineering regarding the exploration and development of unconventional reservoirs. To encourage communication on the advances of research on the rock physics of unconventional reservoirs, a conference on Multiscale Rock Physics for Unconventional Reservoirs was jointly organized by the journals Energies and Advances in Geo-Energy Research. Due to the limitations of movement caused by COVID-19, 21 experts introduced their work online, and the conference featured the latest multiscale theories, experimental methods and numerical simulations on unconventional reservoirs.Cited as: Cai, J., Zhao, L., Zhang, F., Wei, W. Advances in multiscale rock physics for unconventional reservoirs. Advances in Geo-Energy Research, 2022, 6(4): 271-275. https://doi.org/10.46690/ager.2022.04.0

    Production Data Analysis by Machine Learning

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    In this dissertation, I will present my research work on two different topics. The first topic is production data analysis of low-permeability well. The second topic is a quantitative evaluation of key completion controls on shale oil production. In Topic 1, I propose and investigate two novel methodologies that can be applied to improve the results of low-permeability well decline curve analysis. Specifically, I first proposed an iterative two-stage optimization algorithm for decline curve parameter estimation on the basis of two-segment hyperbolic model. This algorithm can be applied to find optimal parameter results from the production history data. By making use of a useful relation that exits between material balance time (MBT) and the original production profile, we propose a three-step diagnostic approach for the preliminary analysis of production history data, which can effectively assist us in identifying fluid flow regimes and increase our confidence in the estimation of decline curve parameters. The second approach is a data-driven method for primary phase production forecasting. Functional principal component analysis (fPCA) is applied to extract key features of production decline patterns on basis of multiple wells with sufficiently long production histories. A predictive model is then built using principal component functions obtained from the training production data set. Finally, we make predictions for the test wells to assess the quality of prediction with reference to true production data. Both methods are validated using field data and the accuracy of production forecasts gives us confidence in the new approaches. In Topic 2, generalized additive model (GAM) is applied to investigate possibly nonlinear associations between production and key completion parameters (e.g., completed lateral length, proppant volume per stage, fluid volume per stage) while accounting for the influence of different geological environments on hydrocarbon production. The geological cofounding effect is treated as a random clustered effect and incorporated in the GAM model by means of a state-of-the-art statistical machine learning method graphic fused LASSO. We provide several key findings on the relation between completion parameters and hydrocarbon production, which provide guidance in the development of efficient completion practices

    Production Data Analysis by Machine Learning

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    In this dissertation, I will present my research work on two different topics. The first topic is production data analysis of low-permeability well. The second topic is a quantitative evaluation of key completion controls on shale oil production. In Topic 1, I propose and investigate two novel methodologies that can be applied to improve the results of low-permeability well decline curve analysis. Specifically, I first proposed an iterative two-stage optimization algorithm for decline curve parameter estimation on the basis of two-segment hyperbolic model. This algorithm can be applied to find optimal parameter results from the production history data. By making use of a useful relation that exits between material balance time (MBT) and the original production profile, we propose a three-step diagnostic approach for the preliminary analysis of production history data, which can effectively assist us in identifying fluid flow regimes and increase our confidence in the estimation of decline curve parameters. The second approach is a data-driven method for primary phase production forecasting. Functional principal component analysis (fPCA) is applied to extract key features of production decline patterns on basis of multiple wells with sufficiently long production histories. A predictive model is then built using principal component functions obtained from the training production data set. Finally, we make predictions for the test wells to assess the quality of prediction with reference to true production data. Both methods are validated using field data and the accuracy of production forecasts gives us confidence in the new approaches. In Topic 2, generalized additive model (GAM) is applied to investigate possibly nonlinear associations between production and key completion parameters (e.g., completed lateral length, proppant volume per stage, fluid volume per stage) while accounting for the influence of different geological environments on hydrocarbon production. The geological cofounding effect is treated as a random clustered effect and incorporated in the GAM model by means of a state-of-the-art statistical machine learning method graphic fused LASSO. We provide several key findings on the relation between completion parameters and hydrocarbon production, which provide guidance in the development of efficient completion practices

    Forecast of lacustrine shale lithofacies types in continental rift basins based on machine learning: A case study from Dongying Sag, Jiyang Depression, Bohai Bay Basin, China

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    Lacustrine shale in continental rift basins is complex and features a variety of mineralogical compositions and microstructures. The lithofacies type of shale, mainly determined by mineralogical composition and microstructure, is the most critical factor controlling the quality of shale oil reservoirs. Conventional geophysical methods cannot accurately forecast lacustrine shale lithofacies types, thus restricting the progress of shale oil exploration and development. Considering the lacustrine shale in the upper Es4 member of the Dongying Sag in the Jiyang Depression, Bohai Bay Basin, China, as the research object, the lithofacies type was forecast based on two machine learning methods: support vector machine (SVM) and extreme gradient boosting (XGBoost). To improve the forecast accuracy, we applied the following approaches: first, using core and thin section analyses of consecutively cored wells, the lithofacies were finely reclassified into 22 types according to mineralogical composition and microstructure, and the vertical change of lithofacies types was obtained. Second, in addition to commonly used well logging data, paleoenvironment parameter data (Rb/Sr ratio, paleoclimate parameter; Sr %, paleosalinity parameter; Ti %, paleoprovenance parameter; Fe/Mn ratio, paleo-water depth parameter; P/Ti ratio, paleoproductivity parameter) were applied to the forecast. Third, two sample extraction modes, namely, curve shape-to-points and point-to-point, were used in the machine learning process. Finally, the lithofacies type forecast was carried out under six different conditions. In the condition of selecting the curved shape-to-point sample extraction mode and inputting both well logging and paleoenvironment parameter data, the SVM method achieved the highest average forecast accuracy for all lithofacies types, reaching 68%, as well as the highest average forecast accuracy for favorable lithofacies types at 98%. The forecast accuracy for all lithofacies types improved by 7%–28% by using both well logging and paleoenvironment parameter data rather than using one or the other, and was 7%–8% higher by using the curve shape-to-point sample extraction mode compared to the point-to-point sample extraction mode. In addition, the learning sample quantity and data value overlap of different lithofacies types affected the forecast accuracy. The results of our study confirm that machine learning is an effective solution to forecast lacustrine shale lithofacies. When adopting machine learning methods, increasing the learning sample quantity (>45 groups), selecting the curve shape-to-point sample extraction mode, and using both well logging and paleoenvironment parameter data are effective ways to improve the forecast accuracy of lacustrine shale lithofacies types. The method and results of this study provide guidance to accurately forecast the lacustrine shale lithofacies types in new shale oil wells and will promote the harvest of lacustrine shale oil globally

    Petrophysical rock typing in Uinta Basin using models powered by machine learning algorithms

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    Petrophysical characterization is key to identifying different rock types for hydrocarbon production optimization. Rock-typing, a petrophysical characterization technique, can be performed using wireline measurements, such as triple combo and special logs; however, this identification needs to be verified using laboratory characterization to enhance the accuracy of rock-typing prediction models. In this work, we implement an integrated characterization workflow for 600 ft of the core from the Uinta Basin, including total organic carbon, source rock analysis, elemental (X-ray Fluorescence) and mineral (Fourier-transform Infrared Spectroscopy) composition, total porosity (High-pressure pycnometer, Nuclear Magnetic Resonance), pore throat size distribution (Mercury Injection Capillary Pressure), and microstructure (Scanning Electron Microscopy). Wireline measurements include the triple combo and the sonic logs. Principal Component Analysis and K-means (as an unsupervised machine learning algorithm) were applied to both datasets (core and log) to cluster and classify different rock types. In parallel, the petrophysical systematic for each rock type was evaluated. The Uinta group is vastly diverse, having a wide range of porosity (2-18%) and TOC (0.5-10%). Three main rock types were identified type 1-siliceous rich, type 2-calcite rich, and type 3-dolomite rich. The relative contribution of types 1, 2, and 3 is 37, 42, and 21 %, respectively. The top section of the analyzed core is dominated by rock type 1, which generally has the highest porosity and relatively higher TOC. Most of the bottom section is carbonate-rich rock types, in which calcite-rich and dolomite-rich layers are interbedded. SEM analyses suggest that a fraction of the porosity is associated with organic matter. Between rock types 3 and 2, further studies indicate that the high dolomite rock type and high total porosity tend to have larger pore size, and better-sorted grains, while the high calcite rock type has lower porosity and small pore size. There is a fair agreement in rock type identification between using core-derived and log-derived models. The Uinta basin leads the hydrocarbon production in Utah. The study provides a comprehensive core analysis dataset highlighting the vertical complexity of the Uinta group. The agreement in rock-typing using core and wireline inputs suggests that log-derived rock-typing can be utilized to identify sweet zones

    Unconventional gas: potential energy market impacts in the European Union

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    In the interest of effective policymaking, this report seeks to clarify certain controversies and identify key gaps in the evidence-base relating to unconventional gas. The scope of this report is restricted to the economic impact of unconventional gas on energy markets. As such, it principally addresses such issues as the energy mix, energy prices, supplies, consumption, and trade flows. Whilst this study touches on coal bed methane and tight gas, its predominant focus is on shale gas, which the evidence at this time suggests will be the form of unconventional gas with the most growth potential in the short- to medium-term. This report considers the prospects for the indigenous production of shale gas within the EU-27 Member States. It evaluates the available evidence on resource size, extractive technology, resource access and market access. This report also considers the implications for the EU of large-scale unconventional gas production in other parts of the world. This acknowledges the fact that many changes in the dynamics of energy supply can only be understood in the broader global context. It also acknowledges that the EU is a major importer of energy, and that it is therefore heavily affected by developments in global energy markets that are largely out of its control.JRC.F.3-Energy securit

    Reservoir Characterisation of Gas Shale through Sedimentary, Mineralogical, Petrophysical and Statistical Rock Types Evaluation

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    The successful exploration and production of the gas shale reservoirs can help to face the current energy crisis. However, shale is a fine-grained heterogeneous rock, so its exploration and development are challenging. This research has provided an integrated method for analysis, evaluation, and synthesis of potential gas shale formations in the Canning Basin, Western Australia. The results form a valuable case study that is applicable to many other sedimentary basins throughout the world

    ROCK TYPING IN ORGANIC SHALES: EAGLE FORD, WOODFORD, BARNETT AND WOLFCAMP FORMATIONS

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    Shales are the most common sedimentary rocks found on earth. Most US shale plays are spatially extensive with regions of different maturity and varying prospects. With increasing understanding of the heterogeneity, microstructure and anisotropy of shales, efforts are now directed to identifying sweet spots and optimizing completion zones in any shale play. Rock typing is a step in this direction. It is becoming an integral part of the unconventional reservoir characterization workflow. In this work, an integrated workflow is presented for rock typing using lab petrophysical measurements, logs, and production data. The key petrophysical parameters used for rock typing are porosity, total organic carbon (TOC), mineralogical compositions and mercury injection capillary pressure (MICP). Principal Component Analysis (PCA) is used to reduce dimensionality of the dataset and improve efficiency of the clustering algorithms. Unsupervised clustering algorithms like K-Means and Self Organizing Maps (SOM) are used to define rock types. The integrated workflow is applied separately for four shale plays namely Barnett, Eagle Ford, Woodford and Wolfcamp. A total of 25 wells with core data are considered for rock typing in the four shale plays. The rock types are upscaled to more than 140 wells representing a 20,000-ft. depth interval. A manual approach would have been prohibitively time-consuming. Rock Type 1 is generally characterized by high porosity, high TOC, and high brittleness. Not surprisingly, Rock Type 1 has the highest positive impact on well productivity. Rock Type 2 has intermediate values of porosity and TOC and thus, moderate source potential and storage. Rock Type 3 has the highest carbonate content (except Eagle Ford) and poor storage (except Eagle Ford) and source rock potential. Classification algorithms like Support Vector Machines (SVM) are used to upscale rock types from core data to logs. The training datasets comprise of depths at which both core and log data are available. Different logs like gamma ray, resistivity, neutron porosity and density are used for upscaling. Finally, a rock type ratio (RTR) is defined based on the fraction of Rock Type 1 over gross thickness. RTR is found to strongly correlate with normalized oil equivalent production rate. The study is unique as it integrates core, log, and production data to identify different rock types. Multiple algorithms are used and the similarity of results between their outputs further bolstered the confidence in the derived rock types. The rock type logs can aid the reservoir or production engineer in optimizing perforation intervals and number of fracture stages. Rock Type 3 is poor reservoir and may not warrant any perforation or fracturing. On the other hand, Rock Type 1 can be selectively perforated and fractured to save cost and maximize production from a well. Other applications of rock typing are 3D reservoir modeling, identifying sweet spots in combination with seismic attributes, new well locations, improved volumetric estimates and uncertainty and risk analysis

    Economic Appraisal of Undeveloped Unconventional Gas: The Bowland United Kingdom Case

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    The estimation of production potential provides the foundation for commercial viability appraisal of natural resources. Due to uncertainty around production assessment approaches in the unconventional petroleum production field, an appropriate production estimation methodology which addresses the requisite uncertainty at the planning stage is required to guide energy policy and planning. This study proposes applying the numerical unconventional production estimation method which relies on geological parameters, (pressure, porosity, permeability, compressibility, viscosity and the formation volume factor) as well as the rock extractive index (a measure of technical efficiency) and develops a model that estimates the appropriate values for four of the parameters required based on a depth correlation matrix while a stochastic process guides the other parameters based on known data range. The developed model is integrated with a numerical model to estimate gas production potential and developed framework is eventually applied to undeveloped shale gas wells located in the Bowland shale, central Britain. The results account for below ground uncertainty and heterogeneity of wells. A sensitivity analysis is applied to consider the relative impacts of individual parameters on production potential. The estimated daily initial gas production rate ranges from 15,000scf to 319,000scf while estimated recovery over 12 years is approximately 1.1bscf in the reference case for wells examined. In relation to cost, A cost analysis is executed, which guides the identification of cost parameters. This study identifies key cost parameters and then develop a non-static model by examining the trends over the years as well as proposes a work break down cost estimation equation. In addition, a methodology in estimating the costs of developing unconventional gas resources based on the production technique is proposed. In addition, the sources of uncertainty in shale gas development cost estimation are examined and identified. It is found that there is an insignificant correlation of cost parameters with oil prices suggest that additional factors need to be analysed. These empirical model and results suggest that the market oil price impact on shale gas production cost although important but restrained by other factors which may include financial revenue hedging programs aimed at securing higher revenues or endogenous efficiency gains which direct production strategy in low oil prices situations. The results from the learning curve and innovation study shows that drilling technology has driven cost reduction and increased lateral lengths while the hydraulic fracturing technology has relied on more material use volumes. The additional demand in stimulation sand and other production materials as well as their disposal can lead to exogenous cost implications. Other expected exogenous cost implications are environmental, regulation and fiscal regimes which can aid or deter technology adoption in different regions. The overarching economic appraisal methodology is based on integration of the depth dependent correlation matrix, bottom up cost estimation and the undeveloped unconventional gas development decision models. Additionally, other input and output parameter scenarios are modelled as well as the impact of carbon emission regulation and mitigation
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