Probabilistic Fracture Research Articles

Modelling the effect of macro-segregation on the fracture toughness of heavy forgings using FFT based crystal plasticity simulations

The local approach has been successful in evaluating the brittle fracture probability of nuclear pressure vessel steels by establishing a link between microstructural defects and the macroscopic fracture behaviour. The evaluation of fracture probabilities relies on the applied stress on the smallest representative elementary volume. A proper description of the stress heterogeneities in polycrystals helps refine the prediction. The current work investigates the effect of carbon macro-segregation in heavy forgings and demonstrates a workflow combining crystal plasticity with the Microstructure Informed Brittle Fracture (MIBF) local approach model in fracture toughness prediction. The microstructural and mechanical properties of low alloy steels with different segregation levels were evaluated. A dislocation-density based crystal plasticity model which contains carbide strengthening contribution was identified and applied for modelling microstructure influence on local stress distributions. Results show that the microstructural evolution observed at high carbon levels has a significant influence on local stress distributions, which in turn affects the fracture toughness. The simulation results also demonstrate that, with proper input of microstructural information, the MIBF model is capable to predict the shift of the brittle-to-ductile transition zone with the variation of carbon and alloying elements and gives insights about factors affecting the resistance of materials.

Laboratory study on degradation of ballast mixed with crumb rubber under impact loads

ABSTRACT This paper evaluates the effect of crumb rubber (CR) on degradation reduction of ballast aggregates subjected to impact loads. A drop-weight impact testing apparatus was developed to test ballast mixed with CR. The probability of corner breakage and bulk fracture was investigated, and the material loss and change in sphericity were identified. The results show that, as CR percentage increases, the corner breakage probability increases first and then decreases, while the bulk fracture probability decreases. The effect of CR on the former is less than that on the latter. The degradation probability of the particles in the size range of 35.5 ~ 45 mm or that of non-cubic shapes is most influenced by CR. The material loss and change in sphericity decrease with increasing CR percentage, and the effect of CR is greatest for the particles in the size range of 25 ~ 35.5 mm and independent of particle shape.

Development of a manufacturer-independent convolutional neural network for the automated identification of vertebral compression fractures in vertebral fracture assessment images using active learning.

Convolutional neural networks (CNNs) can identify vertebral compression fractures in GE vertebral fracture assessment (VFA) images with high balanced accuracy, but performance against Hologic VFAs is unknown. To obtain good classification performance, supervised machine learning requires balanced and labeled training data. Active learning is an iterative data annotation process with the ability to reduce the cost of labeling medical image data and reduce class imbalance. To train CNNs to identify vertebral fractures in Hologic VFAs using an active learning approach, and evaluate the ability of CNNs to generalize to both Hologic and GE VFA images. VFAs were obtained from the OsteoLaus Study (labeled Hologic Discovery A, n=2726), the Manitoba Bone Mineral Density Program (labeled GE Prodigy and iDXA, n=12,742), and the Canadian Longitudinal Study on Aging (CLSA, unlabeled Hologic Discovery A, n=17,190). Unlabeled CLSA VFAs were split into five equal-sized partitions (n=3438) and reviewed sequentially using active learning. Based on predicted fracture probability, 17.6% (n=3032) of the unlabeled VFAs were selected for expert review using the modified algorithm-based qualitative (mABQ) method. CNNs were simultaneously trained on Hologic, GE dual-energy and GE single-energy VFAs. Two ensemble CNNs were constructed using the maximum and mean predicted probability from six separately trained CNNs that differed due to stochastic variation. CNNs were evaluated against the OsteoLaus validation set (n=408) during the active learning process; ensemble performance was measured against the OsteoLaus test set (n=819). The baseline CNN, prior to active learning, achieved 55.0% sensitivity, 97.9% specificity, 57.9% positive predictive value (PPV), F1-score 56.4%. Through active learning, 2942 CLSA Hologic VFAs (492 fractures) were added to the training data-increasing the proportion of Hologic VFAs with fractures from 4.2% to 12.5%. With active learning, CNN performance improved to 80.0% sensitivity, 99.7% specificity, 94.1% PPV, F1-score 86.5%. The CNN maximum ensemble achieved 91.9% sensitivity (100% for grade 3 and 95.5% for grade 2 fractures), 99.0% specificity, 81.0% PPV, F1-score 86.1%. Simultaneously training on a composite dataset consisting of both Hologic and GE VFAs allowed for the development of a single manufacturer-independent CNN that generalized to both scanner types with good classification performance. Active learning can reduce class imbalance and produce an effective medical image classifier while only labeling a subset of available unlabeled image data-thereby reducing the time and cost required to train a machine learning model.

Nutritional status and osteoporotic phenotype of body composition in patients with rheumatoid arthritis

Aim. To evaluate the nutritional status and its relationship with osteoporotic phenotype of body composition in women with rheumatoid arthritis (RA).Material and methods. The study included 91 women aged 40 to 75 years with RA according to ACR/EULAR criteria (2010). A questionnaire, laboratory examination and dual X-ray absorptiometry of whole body, lumbar spine and proximal femur were conducted. Nutritional status was assessed using a MNA (Mini Nutricial Assessment) questionnaire.Results. Risk of malnutrition and malnutrition according to the MNA were detected in 42.9 % and 1.1 % of patients with RA, respectively. These patients differed from those with normal nutritional status with a higher risk of osteoporotic hip fracture according to FRAX (p = 0.035), lower appendicular muscle mass (AMM) (p = 0.048) and lower QOL according to VAS (p = 0.012). A positive correlation was established between the nutritional status by MNA and BMI (r = 0.280; p = 0.007), as well as total muscle mass (r = 0.280; p = 0.008), AMM (r = 0.320; p = 0.002) and AMM index (r = 0.280; p = 0.009). In multivariate logistic regression analysis, age above 55 years (OR = 7.76; 95 % CI: 2.17–27.69), nutritional status by MNA (OR = 0.68; 95 % CI: 0.51–0.92) and AMM index less than 6 kg/m2 (OR = 3.43; 95 % CI: 1.16–10.21) were independent factors associated with osteoporotic phenotype in RA patients.Conclusion. Malnutrition occurred in 44.0 % of RA patients. Women at risk of malnutrition had a higher 10-year probability of hip fracture according to FRAX. Age and AMM index were positively associated, while nutrition status according to MNA was negatively associated with osteoporotic phenotype of body composition.

Application of the soil parameter random field in the 3D random finite element analysis of Guanyinyan composite dam

Due to the inhomogeneous of dam materials, effect of the compaction environment and experimental data errors, the soil properties of the Guanyinyan composite dam present obvious spatial variability, resulting in the occurrence of obvious discordant settlements and cracks during the impounding period. The traditional finite element method (FEM) using inversion parameters that are modified by monitoring displacement back-analysis can neither simulate spatial variability in soil parameters nor predict dam safety before dam displacements occur. In this research, soil parameter random fields are developed considering spatial variability, and a three-dimensional random FEM (RFEM) analysis of the Guanyinyan composite dam is applied. According to soil dry density samples and experimental results, the autocorrelation of the same soil parameter in different positions in dam is determined. The cross-autocorrelation of different soil parameters is conducted using the Nataf transformation. The RFEM analysis shows that the mean values of maximum settlement, the mean values of maximum deformation gradient and area ratio with high hydraulic fracture probability exceeding 0.3 are close to those calculated by inversion parameters. The cumulative frequencies of maximum settlement and maximum deformation gradient show 51% and 33% probabilities of exceeding the inversion calculation results. The area ratio and distribution range of elements with hydraulic failure probabilities greater than 0.2 are significantly larger than those calculated by the inversion parameters. The traditional certain security indexes, which ignore spatial variability of soil parameters, have a certain probability to overestimate the security of the dam. The uncertain security index considering spatial variability of soil parameters can provide a more reasonable and accurate reference for dam safety evaluation before water impounding.

Menopausal hormone therapy reduces the risk of fracture regardless of falls risk or baseline FRAX probability—results from the Women’s Health Initiative hormone therapy trials

SummaryIn a combined analysis of 25,389 postmenopausal women aged 50–79 years, enrolled in the two Women’s Health Initiative hormone therapy trials, menopausal hormone therapy vs. placebo reduced the risk of fracture regardless of baseline FRAX fracture probability and falls history.IntroductionThe aim of this study was to determine if the anti-fracture efficacy of menopausal hormone therapy (MHT) differed by baseline falls history or fracture risk probability as estimated by FRAX, in a combined analysis of the two Women’s Health Initiative (WHI) hormone therapy trials.MethodsA total of 25,389 postmenopausal women aged 50–79 years were randomized to receive MHT (n = 12,739) or matching placebo (n = 12,650). At baseline, questionnaires were used to collect information on falls history, within the last 12 months, and clinical risk factors. FRAX 10-year probability of major osteoporotic fracture (MOF) was calculated without BMD. Incident clinical fractures were verified using medical records. An extension of Poisson regression was used to investigate the relationship between treatment and fractures in (1) the whole cohort; (2) those with prior falls; and (3) those without prior falls. The effect of baseline FRAX probability on efficacy was investigated in the whole cohort.ResultsOver 4.3 ± 2.1 years (mean ± SD), MHT (vs. placebo) significantly reduced the risk of any clinical fracture (hazard ratio [HR] 0.72 [95% CI, 0.65–0.78]), MOF (HR 0.60 [95% CI, 0.53–0.69]), and hip fracture (0.66 [95% CI, 0.45–0.96]). Treatment was effective in reducing the risk of any clinical fracture, MOF, and hip fracture in women regardless of baseline FRAX MOF probability, with no evidence of an interaction between MHT and FRAX (p > 0.30). Similarly, there was no interaction (p > 0.30) between MHT and prior falls.ConclusionIn the combined WHI trials, compared to placebo, MHT reduces fracture risk regardless of FRAX probability and falls history in postmenopausal women.

Research on application of dynamic optimization modification for an involute spur gear in a fixed-shaft gear transmission system

A gear is the key component of a high-efficiency mechanical transmission. Gear modification can effectively improve the dynamic characteristics of the gear transmission system and reduce the probability of vibration deterioration, wear deviation, and fatigue fracture. Based on the lateral-torsional-pendulum coupling nonlinear dynamic model of a gear transmission system, a dynamic optimization modification technology for an involute spur gear is proposed. The parameters of tooth profile modification are selected as independent variables, the dynamic load factor of the system is used as the optimization objective, and the speed and load torque of the driving wheel are used to establish boundary conditions. The vibration response of the system is compared in the time domain, frequency domain, phase plane, and Poincaré section, and the maximum Lyapunov exponent is considered. The dynamic characteristic trends of a gear after dynamic modification are analysed. A vibration performance test bench is built to verify the vibration reduction effect of dynamic optimization modification in a fixed-shaft gear transmission system.

Impacts of fracture properties on the formation and development of stimulated reservoir volume: A global sensitivity analysis

Stimulated reservoir volume (SRV), the highly permeable fracture network created by hydraulic fracturing, is essential for fluid production from low-permeable reservoirs. However, the configuration of SRV and its impacting factors are largely unknown. In this work, we adopt the stochastic discrete fracture network method to mimic natural fractures in subsurface formations and conduct a global sensitivity analysis with the Sobol method. The sensitivity of different fracture properties, including geometrical properties (fracture lengths, orientations, and center positions), mechanical properties (fracture roughness and fracture strength), fracture sealing properties (probabilities of open fractures and segment lengths), and the fracture intensity, are investigated in two and three-dimensional fracture networks. JRC-JCS model is adopted to identify critically stressed fractures. We find that critically stressed fractures compose the backbone of SRV, while partially open fractures can significantly enlarge the size of SRV by connecting more critically orientated fractures. The fracture roughness is the most influential factor for the total length (area) of critically stressed fractures. For the relative increase of SRV (RI) in 2D/3D fracture networks, the probability of open fractures is the most significant factor. The fracture lengths and center positions are essential factors for RI in 2D fracture networks but insignificant in 3D fracture networks. This work provides a realistic scenario of the subsurface structure and systematically investigates the influential factors of SRV, which is useful for estimating the size of SRV and predicting shale gas reservoirs’ production in an accurate and physically meaningful way.

Age targeted human body models indicate increased thoracic injury risk with aging

Objective The objective of this study is to generate age targeted versions of the male and female Global Human Body Models Consortium (GHBMC) occupant human body models (HBMs), to validate each in frontal impacts, and to assess rib fracture probability of each. Methods Six age targeted models were developed based on the GHBMC average male and small female occupant models (M50-O v6.0 and F05-O v6.0, respectively). All age targeted models were modified to represent population means for height, weight, shape, and relevant material properties. The thin plate spline method was used to morph models, and material properties were modified using available literature. Validation focused on chest response. Models were evaluated in a rigid body frontal chest impact at 6.7 m/s. Furthermore, the male and female age targeted models were evaluated against published data from 40 km/hr and 30 km/hr frontal sled tests respectively. Results Chest deflections and landmark kinematics reasonably matched the respective corridors in the M50-O and F05-O aged models. Regional probability of rib fracture was assessed using probabilistic methods based on cortex strain. Increasing rib fracture with age was observed in both impacts for both sexes. For the rigid chest impact, the M50-O 70YO resulted in 10 ribs exceeding 50% probability of fracture whereas the younger ages reported 4 to 6 ribs exceeding the same probability. In the same simulation, the F05-O 70YO resulted in 8 regions exceeding 50% probability of rib fracture as opposed to 3 and 0 such regions at the youngest ages. Sled simulation demonstrated similar trends. The 70YO age adjusted models best aligned with the reported extent of fractures from the referenced PMHS studies, which tend to be composed of subjects of advanced age. Conclusions Age targeted HBMs demonstrated increased fracture probability with age when subjected to equivalent impacts. Gross model kinematics approximate PMHS data but showed little difference between targeted age models. The findings indicate that while gross kinematics are unaffected by age-targeting models, such models can capture trends of increased thoracic injury risk observed in experimental and field studies, and further suggest their potential use to target interventions for vulnerable driving populations, such as older adults.

Comparison of bone mineral density and Fracture Risk Assessment Tool in Saudi women with and without type 2 diabetes mellitus: A cross-sectional study.

To compare the bone mineral density and the fracture risks in Saudi women with and without type 2 diabetes mellitus (T2DM). This cross-sectional study was carried out at Taibah Early Diagnostic Center, Al Madinah Al Munawarah, Saudi Arabia. A total of 465 women with and without T2DM aged ≥40 years who visited the center for a dual-energy X-ray absorptiometry scan between December 2020 and July 2021 were randomly selected. The 10-year probabilities of major osteoporotic fracture (MOF) and hip fracture (HF) were calculated using the Abu Dhabi Fracture Risk Assessment Tool (FRAX) with and without adjustment for T2DM. The adjustment was made by setting rheumatoid arthritis as the equivalent risk for T2DM in the FRAX. Bone mineral density values and the FRAX scores were compared between women with T2DM and non-diabetes. Of 465 women, 214 had T2DM, and 251 were non-diabetics. The mean age of women was 59.42±7.9 years. There were no significant differences in mean age, menopausal status, height, weight, and body mass index between T2DM and non-diabetic women. Bone mineral density values and the unadjusted FRAX scores were comparable between the 2 groups. However, after adjusting FRAX for T2DM, the FRAX for MOF and HF became significantly higher in T2DM women (p=0.000 and p=0.004). In Saudi women with T2DM, unadjusted FRAX underestimated the risk of MOF and HF. Type 2 diabetes mellitus should be included as one of the clinical risk factors for fracture in future versions of the FRAX score.

Research on Throat Speech Signal Detection Based on a Flexible Graphene Piezoresistive Sensor

Aiming at the problem that traditional speech acquisition and recognition are susceptible to environmental noise, this paper proposes a flexible graphene sensor to detect vocal vibration signals. First, the speech detection sensor with a cylindrical microsurface structure substrate is prepared by chemical vapor deposition (CVD) and imprint technology, which greatly improves the conformal coating cover ability and sensitivity of the sensor. In the range of 200–2500 Hz, the average voltage gain of the sensor is ∼48 dB, and this frequency range basically covers the human speech frequency. On this basis, we conducted a bilingual detection (Chinese and English). All data obtained shows that the graphite speech sensor has sufficient sensitivity to extract the characteristics of acoustic waves. At the same time, the proposed cylindrical microsurface structure reduces the probability of random fracture of the graphene layer. In addition, the speech signals collected by a microphone and the flexible graphene speech detection sensor are used to train a neural network. The recognition accuracy of the data set mixed with vocal cord speech signals is 75.9%. The comparison verifies that the signals detected by the sensor have sufficient characteristic information to complete speech recognition tasks.

Diagnosis and Treatment of Children with a Radiological Fat Pad Sign without Visible Elbow Fracture Vary Widely: An International Online Survey and Development of an Objective Definition.

Children often present at the emergency department with a suspected elbow fracture. Sometimes, the only radiological finding is a ‘fat pad sign’ (FPS) as a result of hydrops or haemarthros. This sign could either be the result of a fracture, or be due to an intra-articular haematoma without a concomitant fracture. There are no uniform treatment guidelines for this common population. The aims of this study were (1) to obtain insight into FPS definition, diagnosis, and treatment amongst international colleagues, and (2) to identify a uniform definition based on radiographic measurements with optimal cut-off points via a receiver operating characteristic (ROC) curve. An online international survey was set up to assess the diagnostic and treatment strategies, criteria, and definitions of the FPS, the probability of an occult fracture, and the presence of an anterior and/or posterior FPS on 20 radiographs. Additionally, the research team performed radiographic measurements to identify cut-off values for a positive FPS, as well as test–retest reliability and inter-rater reliability via intraclass correlation coefficients (ICC). A total of 133 (paediatric) orthopaedic surgeons completed the survey. Definitions, further diagnostics, and treatments varied considerably amongst respondents. Angle measurements of the fat pad as related to the humeral axis line showed the highest reliability (test–retest ICC, 0.95 (95% CI 0.88–0.98); inter-rater ICC, 0.95 (95% CI 0.91–0.98)). A cut-off angle of 16° was defined a positive anterior FPS (sensitivity, 1.00; specificity, 0.87; accuracy, 99%), based on the respondents’ assessment of the radiographs in combination with the research team’s measurements. Any visible posterior fat pad was defined as a positive posterior FPS. This study provides insight into the current diagnosis and treatment of children with a radiological fat pad sign of the elbow. A clear, objective definition of a positive anterior FPS was identified as a ≥16° angle with respect to the anterior humeral line.

975 RELATIONSHIP BETWEEN HAND GRIP STRENGTH (HGS) AND 10 YEAR PROBABILITY OF MAJOR OSTEOPOROTIC FRACTURES IN OLDER

Abstract Introduction HGS has been shown to be associated with adverse outcomes in a wide range of conditions. It has also been found to be associated with bone mineral density and is inversely associated with risk of osteoporotic fractures. The aim of this study was to determine the association between HGS and 10 year probability of sustaining a major osteoporotic fractures in older inpatient and to evaluate the effect of gender on this association. Method This was a cross-sectional, observational analysis of older patients admitted into hospital between September and November 2021. HGS was measured in these patients using the JAMAR hydraulic hand held dynamometer. The Southampton protocol was used. The FRAX UK tool (without BMD) was used to calculate 10 year probability of major osteoporotic fractures. Patients were included if they were 60 years and above. Younger patients were excluded as were stroke patients. Patients with incomplete data were excluded from analysis. The SPSS 27 package was used for statistical analysis. Baseline characteristics were calculated using descriptive statistics. Pearson’s correlation coefficient and linear regression were used to calculate correlation. Results 104 patients were analysed—41 males and 63 females. Mean age was 83 years (SD 8.2). Grip strength was inversely correlated with FRAX UK in predicting 10 year probability of major osteoporotic fractures in all patients, male patients and female patients (r = −0.452; p < 0.001, r = −0.351; p = 0.02 and r = −0.271; p = 0.03 respectively). Conclusion Grip strength is negatively associated with increased probability of major osteoporotic fractures in older inpatients. Reference(s) Ma Y, Fu L, Jia L, et al. Muscle strength rather than muscle mass is associated with osteoporosis in older Chinese adults. J Formos Med Assoc. 2018;117(2):101–108. doi:10.1016/j.jfma.2017.03.004

Effect of cyclic tests on the shape memory performance of GO‐CF hybrid‐reinforced SMPC

AbstractThis study investigated the effect of cyclic tests on the shape memory performance of graphene oxide‐carbon fiber (GO‐CF) hybrid‐reinforced shape memory polymer composite (SMPC). The shape memory performance was evaluated by four indexes: shape fixation rate, shape recovery rate, recovery time, and maximum recovery force. The results showed that the service life of the GO‐CF hybrid‐reinforced SMPC prepared by the vacuum infiltration hot‐press forming experimental system process (VIHPS) was 16 cycles. After 16 repetitions, the shape fixation rate decreased by approximately 6.6% and the shape recovery rate decreased by approximately 14.0%. At this time, the shape recovery rate was lower than 90%. The recovery time increased by 62.2% and the maximum recovery force decreased by 70.1%. The maximum recovery force exhibited the highest performance attenuation. Scanning electron microscopy (SEM) was used to photograph and analyze the microstructures of the samples under various cyclic tests. It was concluded that the main factor affecting the shape memory performance of GO‐CF hybrid‐reinforced SMPC is the life of fiber bundle. During cyclic testing, the fiber was prone to kinks, tensile damage and other defects, which increased the probability of reducing the length of the fiber bundle. Therefore, the probability of fiber fracture was improved, and the shape memory performance of the GO‐CF hybrid‐reinforced SMPC was attenuated.

Association of Thiazide Use in Patients with Hypertension with Overall Fracture Risk: A Population-Based Cohort Study.

Thiazide diuretics have long been widely used as antihypertensive agents. In addition to reducing blood pressure, thiazides also control calcium homeostasis and increase bone density. We hypothesized that the use of thiazides in patients with hypertension would reduce overall fracture risk. We used the Taiwan National Health Insurance Research Database to find patients with a hypertension diagnosis who accepted antihypertensive treatment from 2000 to 2017. The patients were further classified into thiazide users and nonthiazide users. Multivariable Cox regression analysis and Kaplan–Meier survival analysis were performed to estimate the adjusted hazard ratios (aHRs) and cumulative probability of fractures. After 1:1 propensity score matching by sex, age, urbanization level of place of residence, income, comorbidities, and medications, there were 18,483 paired thiazide users and non-users, respectively. The incidence densities of fractures (per 1000 person-months) were 1.82 (95% CI: 1.76–1.89) and 1.99 (95% CI: 1.92–2.06) in the thiazide and nonthiazide groups, respectively. The results indicated a lower hazard ratio for fractures in thiazide users (aHR = 0.93, 95% CI: 0.88–0.98). Kaplan–Meier survival analysis revealed a significantly lower cumulative incidence of fractures in the thiazide group (log-rank test; p = 0.0012). In conclusion, our results reveal that thiazide use can reduce fracture risk. When antihypertensive agents are being considered, thiazide may be a better choice if the patient is at heightened risk of fracture.

A Machine Learning Algorithm to Estimate the Probability of a True Scaphoid Fracture After Wrist Trauma

To identify predictors of a true scaphoid fracture among patients with radial wrist pain following acute trauma, train 5 machine learning (ML) algorithms in predicting scaphoid fracture probability, and design a decision rule to initiate advanced imaging in high-risk patients. Two prospective cohorts including 422 patients with radial wrist pain following wrist trauma were combined. There were 117 scaphoid fractures (28%) confirmed on computed tomography, magnetic resonance imaging, or radiographs. Eighteen fractures (15%) were occult. Predictors of a scaphoid fracture were identified among demographics, mechanism of injury and examination maneuvers. Five ML-algorithms were trained in calculating scaphoid fracture probability. ML-algorithms were assessed on ability to discriminate between patients with and without a fracture (area under the receiver operating characteristic curve), agreement between observed and predicted probabilities (calibration), and overall performance (Brier score). The best performing ML-algorithm was incorporated into a probability calculator. A decision rule was proposed to initiate advanced imaging among patients with negative radiographs. Pain over the scaphoid on ulnar deviation, sex, age, and mechanism of injury were most strongly associated with a true scaphoid fracture. The best performing ML-algorithm yielded an area under the receiver operating characteristic curve, calibration slope, intercept, and Brier score of 0.77, 0.84,-0.01 and 0.159, respectively. The ML-derived decision rule proposes to initiate advanced imaging in patients with radial-sided wrist pain, negative radiographs, and a fracture probability of ≥10%. When applied to our cohort, this would yield 100% sensitivity, 38% specificity, and would have reduced the number of patients undergoing advanced imaging by 36% without missing a fracture. The ML-algorithm accurately calculated scaphoid fracture probability based on scaphoid pain on ulnar deviation, sex, age, and mechanism of injury. The ML-decision rule may reduce the number of patients undergoing advanced imaging by a third with a small risk of missing a fracture. External validation is required before implementation. Diagnostic II.

Evaluation of fractures using conventional and FMI logs, and 3D seismic interpretation in continental tight sandstone reservoir

Abstract Due to the complex pore structures and strong heterogeneity of fractured reservoirs, it is a hot and difficult point in petroleum geology to identify fractures using logging principles. In this paper, taking the tight sandstone reservoir of the Chang 8 Member in the Huanjiang Oilfield as an example, field outcrops, cores, thin sections, and logging identification methods were used for quantitative description and fine logging evaluation of fractures. The research shows that high-angle, medium-low-angle, near-vertical, and horizontal fractures are developed in the Chang 8 Member of the Huanjiang Oilfield. The main ones are high-angle fractures, followed by horizontal fractures with a low degree of fillings. Under the constraints of core and imaging logging data, three fracture sensitivity logging parameters of acoustic wave time difference, natural gamma, and dual induction-octalateral resistivity were optimized, and a comprehensive fracture probability index was proposed. Seventy-nine fracture development intervals were identified based on log curve characteristics and fracture probability indexes. The coincidence rate of fracture logging identification results with the core observation and imaging logging interpretation is 80.6%. The research results can provide a theoretical basis for the efficient development of fractured continental tight sandstone reservoirs in similar areas.

Reverse engineering the FRAX algorithm: Clinical insights and systematic analysis of fracture risk.

The Fracture Risk Assessment Tool (FRAX) is a computational tool developed to predict the 10-year probability of hip fracture and major osteoporotic fracture based on inputs of patient characteristics, bone mineral density (BMD), and a set of seven clinical risk factors. While the FRAX tool is widely available and clinically validated, its underlying algorithm is not public. The relative contribution and necessity of each input parameter to the final FRAX score is unknown. We systematically collected hip fracture risk scores from the online FRAX calculator for osteopenic Caucasian women across 473,088 unique inputs. This dataset was used to dissect the FRAX algorithm and construct a reverse-engineered fracture risk model to assess the relative contribution of each input variable. Within the reverse-engineered model, age and T-Score were the strongest contributors to hip fracture risk, while BMI had marginal contribution. Of the clinical risk factors, parent history of fracture and ongoing glucocorticoid treatment had the largest additive effect on risk score. A generalized linear model largely recapitulated the FRAX tool with an R2 of 0.91. Observed effect sizes were then compared to a true patient population by creating a logistic regression model of the Study of Osteoporotic Fractures (SOF) cohort, which closely paralleled the effect sizes seen in the reverse-engineered fracture risk model. Analysis identified several clinically relevant observations of interest to FRAX users. The role of major osteoporotic fracture risk prediction in contributing to an indication of treatment need is very narrow, as the hip fracture risk prediction accounted for 98% of treatment indications for the SOF cohort. Removing any risk factor from the model substantially decreased its accuracy and confirmed that more parsimonious models are not ideal for fracture prediction. For women 65years and older with a previous fracture, 98% of FRAX combinations exceeded the treatment threshold, regardless of T-score or other factors. For women age 70+ with a parent history of fracture, 99% of FRAX combinations exceed the treatment threshold. Based on these analyses, we re-affirm the efficacy of the FRAX as the best tool for fracture risk assessment and provide deep insight into the interplay between risk factors.

Update of the fracture risk prediction tool FRAX: a systematic review of potential cohorts and analysis plan.

The availability of the fracture risk assessment tool FRAX® has substantially enhanced the targeting of treatment to those at high risk of fracture with FRAX now incorporated into more than 100 clinical osteoporosis guidelines worldwide. The aim of this study is to determine whether the current algorithms can be further optimised with respect to current and novel risk factors. A computerised literature search was performed in PubMed from inception until May 17, 2019, to identify eligible cohorts for updating the FRAX coefficients. Additionally, we searched the abstracts of conference proceedings of the American Society for Bone and Mineral Research, European Calcified Tissue Society and World Congress of Osteoporosis. Prospective cohort studies with data on baseline clinical risk factors and incident fractures were eligible. Of the 836 records retrieved, 53 were selected for full-text assessment after screening on title and abstract. Twelve cohorts were deemed eligible and of these, 4 novel cohorts were identified. These cohorts, together with 60 previously identified cohorts, will provide the resource for constructing an updated version of FRAX comprising 2,138,428 participants with a follow-up of approximately 20 million person-years and 116,117 documented incident major osteoporotic fractures. For each known and candidate risk factor, multivariate hazard functions for hip fracture, major osteoporotic fracture and death will be tested using extended Poisson regression. Sex- and/or ethnicity-specific differences in the weights of the risk factors will be investigated. After meta-analyses of the cohort-specific beta coefficients for each risk factor, models comprising 10-year probability of hip and major osteoporotic fracture, with or without femoral neck bone mineral density, will be computed. These assembled cohorts and described models will provide the framework for an updated FRAX tool enabling enhanced assessment of fracture risk (PROSPERO (CRD42021227266)).

Evolution of the stress and strain field in the tyra field during the Post-Chalk Deposition and seismic inversion of fault zone using informed-proposal Monte Carlo

When hydrocarbon reservoirs are used as a CO2 storage facility, an accurate uncertainty analysis and risk assessment is essential. An integration of information from geological knowledge, geological modelling, well log data, and geophysical data provides the basis for this analysis. Modelling the time development of stress/strain changes in the overburden provides prior knowledge about fault and fracture probability in the reservoir, which in turn is used in seismic inversion to constrain models of faulting and fracturing. One main problem in solving large scale seismic inverse problems is high computational cost and inefficiency. We use a newly introduced methodology - Informed-proposal Monte Carlo (IPMC) - to deal with this problem, and to carry out a conceptual study based on real data from the Danish North Sea. The result outlines a methodology for evaluating the risk of having sub-seismic faulting in the overburden that potentially compromises the CO2 storage of the reservoir.