Production Data Research Articles

OPTIMIZATION OF MIB-02 WELL BY CONVERTING THE ACTIVATION MODE USING PROSPER SOFTWARE

Mibale oil field considered a mature oil field, located offshore in the coastal basin of the DR Congo and discovered in 1973, has seen an evolution in its production since it began operation in 1976, starting from an initial rate of 10,000 BOPD with 3 producing wells to the current production of 5,905 BOPD from 14 wells. In 2021, the field had 393 MMstb of oil reserves in the reservoir. A thorough study of the production system was carried out, focusing on the five best producing wells, including well MIB-02. Despite traversing layers of the reservoir with high oil potential, the well is underperforming with a respective flow rate of 351 BOPD and a 60% WOR using an electric submersible pump. Following this low production, various studies were conducted, including the study on converting the activation method by proceeding with nodal analysis technique. The use of nodal analysis proved crucial in identifying the shortcomings of the production system, combining field history with analysis of production, well test, pressure, and completion data. The IPM Prosper software enabled a thorough analysis, revealing that MIB-02 had a production potential greater than its current performance. This performance was revealed after evaluation and interpretation of IPR, VLP curves and sensitivities of variables such as water cut and pressure at the first node. After interpreting these curves, the MIB-02 well had the production capacity of 4100.62 BOPD and 12463.9 BWPD. By converting the submersible electric pump activation mode to gas-lift and exploring various scenarios, production was improved with an oil flow rate of 1244.9 BOPD, water flow of 2539 BWPD, to a gas flow of 0.37346 MMscf/day and 67.1% water cut. Despite these improvements, excessive water production remains a challenge while also highlighting the complexity of the production system and the limitations of the analysis technique. Therefore, further studies can be envisaged by modeling and simulating the reservoir, considering well interventions and quantifying the remaining reserves today. This more comprehensive approach will enable a clearer vision and help decide the best optimization strategies for the Mibale field production system.

The effect of preferential associations on the reproductive performance of group-housed sows

The investigation of social bonds as a measure to improve reproduction in farmed species is an underrepresented research area. This study investigated the effects of preferential associations between group housed sows (average herd size, n = 59) on stillborn and crushed piglet numbers. Preferential associations were described as resting within < 1 m of a conspecific in which the proximity was tolerated > 60 s. The study occurred over 63 consecutive days, broken down into three 21-day periods referred to as cycles. The 21-day cycles represent the time between reintegrating events. Seven days per cycle were selected for observations providing 63 h of footage covering the functional areas of the barn. Production data were taken from one farrowing that occurred after a sow had been transferred from the barn to the farrowing house during the study period. For group-level analysis, the sows were categorised as socially prominent or non-socially prominent. Social prominence is defined as an individual that engages in significantly higher levels of interactions than their sub-group conspecifics. The subgroups were determined by our previous work that identified assortment by social connectivity within the same study herd. Each subgroup was defined as a k-core, in which the k-value represents the level of connectivity of those in the group (i.e., subgroup K1 means sows are connecting with at least one other conspecific). For individual-level analysis, sows were categorised as a sow with stillborn (at least one stillborn piglet) or a sow without stillborn (no stillborn piglets) and as a crushing sow (at least one crushed piglet) or a non-crushing sow (no crushed piglets). Degree centrality was applied to determine the number of interactions that individuals initiated and received. Results showed no overall effects of social prominence on live-born piglets (p = 0.436). Socially prominent sows demonstrated a lower mean rate of stillborn than non-socially prominent sows. Sows with stillborn had significantly lower degree centrality than sows without stillborn (p < 0.05). The numbers of crushed piglets between socially prominent and non-socially prominent sows were variable with crushing sows demonstrating significantly higher degree centrality than non-crushing sows (p < 0.05). Overall, the evidence does not provide a clear relationship between social prominence during gestation and the reproductive outputs of sows. Therefore, further work is required to validate the effects of social position in affiliative networks on the production indices of farmed pigs.

Microbial community composition predicts bacterial production across ocean ecosystems.

Microbial ecological functions are an emergent property of community composition. For some ecological functions this link is strong enough that community composition can be used to estimate the quantity of an ecological function. Here, we apply random forest regression models to compare the predictive performance of community composition and environmental data for bacterial production (BP). Using data from two independent long-term ecological research sites - Palmer LTER in Antarctica and Station SPOT in California - we found that community composition was a strong predictor of BP. The top performing model achieved an R2 of 0.84 and RMSE of 20.2pmolL-1hr-1 on independent validation data, outperforming a model based solely on environmental data (R2= 0.32, RMSE = 51.4pmolL-1hr-1). We then operationalized our top performing model, estimating BP for 346 Antarctic samples from 2015-2020 for which only community composition data were available. Our predictions resolved spatial trends in BP with significance in the Antarctic (P value = 1 x 10-4) and highlighted important taxa for BP across ocean basins. Our results demonstrate a strong link between microbial community composition and microbial ecosystem function and begin to leverage long-term datasets to construct models of BP based on microbial community composition.

Comparative assessment of gridded soil moisture data products (2000–2019) for drought detection: A study over Indian region

Comparative assessment of gridded soil moisture data products (2000–2019) for drought detection: A study over Indian region

A regional analysis of climate change effects on global snow crab fishery

AbstractThe snow crab fishery faces increasing vulnerability to environmental factors, yet the literature on the relationship between climate change and snow crab harvest remains limited. This study estimates snow crab harvest functions using climate change indicators with unbalanced panel data of snow crab production from the eastern Bering Sea (Alaska), the southern Gulf of St. Lawrence (Canada), the Sea of Japan, and the Barents Sea (Norway‐Russia). The relationship between snow crab biomass, stock, and catch is analyzed and the endogeneity of stock in the harvest function is also addressed using climate change indicators as instrumental variables (IVs). The results show that the extent of Arctic sea ice is effective in addressing the endogeneity, and the random effects IV model with error components two stage least squares estimator performs the best to control heterogeneity. A 1% increase in snow crab fishing effort is associated with a 0.42% increase in snow crab harvest, and a 1% increase in snow crab stock causes a 0.98% increase in snow crab harvest. The reported estimates indicate a large stock‐harvest elasticity and provide supporting evidence to prioritize stock enhancement in snow crab fishery policy designs to maintain stocks at sustainable levels and minimize government expenditures on subsidies.Recommendations This study explores how snow crab harvests are influenced by snow crab populations and fishing efforts in the context of global warming across various global regions, including the Bering Sea, the Gulf of St. Lawrence, the Sea of Japan, and the Barents Sea. A 1% increase in fishing effort is associated with a 0.42% increase in harvest, while a 1% increase in snow crab population leads to a 0.98% increase in harvest, showing a high dependency on snow crab biomass. Arctic sea ice extent is identified as a crucial climate factor affecting snow crab biomass and harvests, making it a valuable variable for understanding and managing snow crab populations. The study supports the prioritization of stock enhancement policies by fishery agencies and suggests standardizing how fishing effort is measured across different regions to improve snow crab fishery management and future research.

Distributed assembly shop scheduling problem for complex products considering multiskilled worker assignment and transportation time

The distributed assembly hybrid flow shop scheduling problem (DAHFSP) is a type of distributed shop scheduling problem, and each distributed shop can be regarded as a hybrid flow shop. In distributed assembly processes for complex products such as satellites and missiles, transportation time and worker assignment have important effects on production scheduling. Based on real production situations, this study proposes a novel DAHFSP considering worker assignment and transportation time, aiming to minimise the makespan and the imbalance degree of worker workloads. First, to solve these problems, we construct a mathematical model and design a two-layer chromosome coding scheme including worker assignment and task sequence. Then, in the local search stage, we propose a mutation-based search method and an elite search method. On that basis, we propose a multi-objective evolutionary algorithm with reinforced elite retention strategy (MOEA-RERS). Finally, based on a set of 12 test instances generated by actual enterprise production data, we compare the MOEA-RERS algorithm with five multi-objective evolutionary algorithms. The results show that the MOEA-RERS algorithm is superior to other algorithms in terms of solution quality and distribution.

ROBUST GEOGRAPHICALLY WEIGHTED REGRESSION WITH LEAST ABSOLUTE DEVIATION (LAD) ESTIMATION AND M-ESTIMATION ON GRDP OF WEST JAVA PROVINCE

Geographically Weighted Regression (GWR) is an analytical method for data that contains spatial heterogeneity effects. However, parameter estimation in the GWR model has a weakness, namely it is prone to outliers and can cause the parameter estimation to be biased. This can be overcome by the Robust Geographically Weighted Regression (RGWR) method which is more robust against the presence of outliers. This method is suitable for Gross Regional Domestic Product (GRDP) data in West Java Province, which contains outliers and also has spatial effects. The data used in this study are secondary data obtained from the Central Statistics Agency (BPS) of West Java Province. The purpose of this study is to compare the Robust Geographically Weighted Regression (RGWR) method with the Least Absolute Deviation (LAD) Estimation and M-estimation and also to find out the factors that affect the Gross Regional Domestic Product (GRDP) in West Java Province in 2021 based on the model resulting from. Selection of the best model is seen based on the value of the coefficient of determination (R2) and Mean Squared of Error (MSE). The research results show that the Robust Geographically Weighted Regression (RGWR) method with M-estimation is much more effective in estimating the distribution of GRDP in West Java Province in 2021, seen from the larger coefficient of determination and the smaller Mean Square Error (MSE). The variables that have a significant influence on GRDP in West Java Province in 2021 are the variables of foreign investment and local income.

WEB BASED ARCHIVE MANAGEMENT APPLICATION CASE STUDY IN CIJERAH VILLAGE (E-ARCHIVE)

This research has the goal of producing a mathematics learning medium in which the game contains mathematical material, namely matrix material which is designed with various challenges, questions, and in-game events related to the matrix. The research method used is the research and development method. The research procedure is a simplification of Sugiyono's development steps, namely potentials and problems, data collection, product design, design validation, design improvement, product trials, product revisions, usage trials, and conclusions. The results of the study show that educational game media has proper characteristics. This can be seen from the evaluation of the experts, media experts obtained a proportion of 86.7% with a proper rating and material experts obtained a proportion of 88.3% with the assessment criteria, and math teachers of 90.85% with the appropriate assessment criteria. Contribution of the research is that this educational game media can be used by teachers in the learning process for matrix materials. This helps the teacher so that the learning process is more practical because the material is packaged in a game. In addition, this educational game media can also help students learn independently.

Temperature retrieval of near space with the combined use of O2(a1Δg) and O2(b1∑+ g) dayglow emissions under self-absorption effect correction

Atmospheric temperature information in the near space is of great academic significance and engineering value to support the development and utilization of the near space. Based on the theory of O2 molecular dayglow spectroscopy and the mechanism of atmospheric radiative transfer, a method is proposed for the joint retrieval of temperature profiles in the near space using O2(a1Δg) and O2(b1∑+g) bands dayglow spectroscopy signal with the self-absorption effect. First, the temperature dependence of O2(a1Δg) and O2(b1∑+g) bands dayglow is investigated, and the influence of the self-absorption effect on the radiative transfer characteristics is analyzed in the limb-view mode. Then, with the use of the onion peeling algorithm, the dayglow emission spectra signals of the O2(a1Δg) and O2(b1∑+g) bands measured by the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) in the limb-viewing mode were processed, and combined with optimization algorithms, the temperature profiles from 35 km to 120 km is successfully retrieved. Finally, the accuracy and reliability of the self-absorption effect correction as well as the joint temperature retrieval were verified by comparing with temperature product data from remote sensing satellites such as Sounding of the Atmosphere using Broadband Emission Radiometry (SABER), Atmospheric Chemistry Experiment Fourier-Transform Spectrometer (ACE-FTS), and Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). The error analysis shows that the temperature retrieval error after correction for the self-absorption effect is about 3 K minimum and 20 K maximum.

On-demand high-resolution areal scouting via unmanned aerial vehicles enables sustainable near-surface geoscience data acquisition

An accurate data acquisition plan is critical for near-surface geoscience investigations. Detailed understanding of the terrain and land cover of the survey area is essential to designing a data acquisition layout that efficiently meets the objective while mapping potential obstacles. Using satellite-based data is the most affordable and straightforward method to obtain this information. However, near-surface geoscience investigations demand a higher-resolution map than commonly available satellite imagery to design a detailed acquisition plan. This paper presents a concept that utilizes unmanned aerial vehicles (UAVs) to successfully and sustainably perform on-demand high-resolution areal scouting to aid near-surface geoscience data acquisition campaigns and seismic drone deployment. We share the results of two field trial cases where the presented concept was evaluated. Case A is a 16 km2 extensive outcrop investigation in an arid desert environment. The initial satellite-based acquisition plan was changed considerably when UAV data sets became available. This led to an approximately 60% improvement in data productivity and efficiency, effectively reducing the carbon footprint of conventional scouting operations. Case B utilizes high-resolution areal scouting to determine the safe landing zone for autonomous seismic drone swarms to further support sustainable geophysical data acquisition. The UAV data enable seismic drone swarms to land optimally on preprogrammed landing points while navigating subsatellite-resolution obstacles. These two successful cases suggest that analogous productivity and efficiency improvements may be achieved in industrial-scale land seismic data acquisition planning that utilizes the UAV data set.

IoT based MPPT techniques for photovoltaic frameworks management under different environmental conditions: a review

&lt;p&gt;Solar energy (SE) is the most attractive form of renewable energy (RE) source for electrification. To harness SE, the photovoltaic (PV) system is required towards converting sunlight into direct electricity. The PV frameworks can be placed in areas with high energy potential. The performance of PV frameworks is complex work which depends on various parameters of the frameworks and their operations. The performance of PV frameworks can be evaluated using MATLAB/Simulink platform and real-time implementation. In this research article, the internet of things (IoT) is investigated to regulate and monitor PV system performance in various environments. IoT-based maximum power point tracking (MPPT) technology improves the response of real-time operating characteristics which makes it possible to control remote PV systems management, quickly diagnose problems and maintain them effectively. Additionally, it allows for recording production and performance data for analysis.&lt;/p&gt;

An improved Harris hawks optimizer with enhanced logarithmic spiral and dynamic factor and its application for predicting molten iron temperature in the blast furnace

AbstractIn response to the problem of poor search performance and difficulty in escaping from the local optimum in the Harris hawks optimizer, an improved Harris hawks optimizer with enhanced logarithmic spiral and dynamic factor (IHHO‐ELSDF) is proposed in this paper. The enhanced logarithmic spiral mechanism is adopted in the exploration phase, and its main feature is the use of an improved opposite‐learning hybrid logarithmic spiral mechanism to search for more promising regions. The dynamic factor is used to replace the escaping energy to improve the global search capability of the algorithm, and it can better balance exploration and exploitation. In addition, a random distribution strategy is proposed for the exploitation phase to avoid falling into the local optimum. Based on 23 classical test functions, the influence of the distribution probability, the three improved mechanisms, and the exploration–exploitation ratio in IHHO‐ELSDF are analyzed. Subsequently, IHHO‐ELSDF is subjected to a comparative analysis with 17 algorithms on the IEEE CEC2022 benchmark suite. These tests show that IHHO‐ELSDF outperforms most competitors in numerical optimization. Furthermore, to assess its applicability in real‐world problems, IHHO‐ELSDF is employed to optimize parameters in the wavelet neural network used for molten iron temperature prediction. The simulation results based on real production data show that the proposed prediction model achieves a high prediction precision with , , and .

A methodology for evaluating feature selection and clustering methods with project-specific requirements

This paper describes a methodology for ranking feature selection and clustering methods with user-specific preferences and taking data properties into account. For a better understanding of this paper, the developed methodology is referred to as the Two Machine Learning Procedures, Preferences and Properties (2ML3P) methodology. The 2ML3P methodology aims to support users from multiple domains, such as engineers, who have little expertise in machine learning (ML). It is also independent from the disciplinary core competencies of the manufacturer, with a strong focus on employability in small and mid-sized enterprises (SME). The foundation of the methodology to evaluate the combination of the two machine learning classes is described. It focuses on a range of feature selection and clustering methods, their limitations, and their challenges. The paper covers the concept phase by defining the inputs, such as the specific characteristics of machine learning classes or the properties of the production data and the user preferences. With applied methodologies such as the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS), the preferences of the user as valid input are integrated. The scientific contribution of this methodology is the approach to include user preferences and specific data properties in the selection process of two ML methods. As digitalisation progresses, making data-driven decisions in the domains of production and logistics is a goal for many SMEs. This methodology can support a data-driven decision-aid model by providing a guided method, which requires relatively little ML knowledge on the part of the engineer. It allows the user(s) to select the best suited combination of ML methods for a clustering use case.

Quantifying the Effects of Interwell Communication Using Dynamic Fluid-in-Place Calculations

Summary Multifractured horizontal wells (MFHWs) completed in the same reservoir layer, or different reservoir layers, commonly experience interwell communication through hydraulic fractures. For example, after a well is placed on production, its production performance can be impacted by communication with an offsetting well placed on production after it. The degree of communication between wells is important to quantify for the purposes of well production forecasting, reserves estimation, completions, and well spacing design optimization. In this study, dynamic fluid-in-place calculations, performed using the impacted producing well rates and flowing pressures, are applied to quantify the effect of communication with an offset-producing well on the impacted well-contacted fluid-in-place estimates. Agarwal (2010) demonstrated that pressure transient analysis theory can be used to derive the volume of fluid in place contacted by a well (CFIP) over time during constant rate, transient production. The method was later extended to variable-rate/pressure scenarios. However, all previous applications of Agarwal’s method were for single, isolated wells and assumed single-phase flow of oil and gas. To evaluate the usefulness of the method for modern development scenarios, it is extended to allow for quantification of interwell communication during flowback, for which single-phase flow of water before the breakthrough of formation fluids may precede multiphase flow of formation and fracturing fluids, and for analysis of multiphase data. Analysis of flowback data enables early-time identification and quantification of interference effects. Multiple numerical simulation cases are generated to simulate different degrees of communication for the case of a two-phase flow of oil and water. Wells are assumed to be communicating through a hydraulic fracture with a specified transmissibility multiplier (Tmult) used to adjust the amount of interwell communication. Corrections for multiphase flow in the CFIP method are performed using two different methods—the total volumetric flow rate (combined phase) approach and the multiphase pseudovariable approach. The CFIP diagnostic plot (i.e., log-log plot of CFIP vs. material balance time) is applied to the impacted producing well to evaluate the CFIP trend before and after offset well production and the magnitude of CFIP change. The practical application of the method is demonstrated with field cases. From the simulation cases, it is observed that, after the offset well is placed on production, a reduction of CFIP for the impacted producing well occurs (rapidly decreasing at first and then stabilizing after a transition period) proportional to productivity index reduction. The loss in CFIP for the impacted producing well can be determined simply by estimating its CFIP immediately before and after offset well production. For high connectivity (Tmult&amp;gt; 0.25) scenarios, application of the combined phase approach resulted in estimates of the impacted well CFIP reduction of ~46–50%, whereas application of the modified pseudovariable approach resulted in estimates of ~49–51%. For the low connectivity case (Tmult = 0.001), these estimates were ~11% and ~9%, respectively, for the two approaches. Therefore, for the simulation cases studied herein, the two approaches agreed within acceptable error. Numerical simulation was also used to verify the absolute change in CFIP using these two approaches for correcting for multiphase flow. The practical application of the modified CFIP method was demonstrated using two field cases with early-time production. Both field cases demonstrated that changes in CFIP for the impacted well can be unambiguously interpreted. In the first field case corresponding to early-time production data (gas and water) associated with Well 23 of the SPE data repository, the reduction in CFIP of the impacted producing well was estimated to be ~37% using the combined phase approach. In the second field case, for which a producing well completed in a low-permeability gas condensate reservoir is impacted by placing multiple offset wells on production at the same time, the reduction in CFIP of the impacted well was estimated to be ~20% using the combined phase approach. In this study, we demonstrate for the first time that CFIP calculations can be applied to quantify interwell communication between two wells during flowback or early-time production when multiphase flow occurs in the reservoir.

History of the Surveillance, Epidemiology, and End Results (SEER) Program.

The Surveillance, Epidemiology, and End Results (SEER) Program established in 1973 was the first laboratory for experimenting with new methods for cancer data collection and translating the data into population-based cancer statistics. The SEER Program staff have been instrumental in the development of the International Classification of Disease-Oncology and successfully implemented the routine collection of anatomic and prognostic cancer stage at diagnosis. Currently the program consists of 21 central registries that generate cancer statistics covering more than 48% of the US population and an additional 10 research support registries contributing to certain research projects, such as the National Childhood Cancer Registry. In parallel with the geographical expansion, the program built an architecture of methods and tools for population-based cancer statistics, with SEER*Explorer as the most recent online tool for descriptive statistics. In addition, SEER releases annual updates for a comprehensive data product line, which includes SEER*Stat databases with an annual caseload of more than 800 000 incident cases. Furthermore, the program developed a full suite of analytical applications for population-based cancer statistics that include Joinpoint (regression-based trend analysis), DevCan (risk of diagnosis and death), CanSurv (survival models), and ComPrev and PrejPrev (cancer prevalence), among others. The future of the SEER Program is closely aligned to the overall goals of the "war on cancer." The program aims to release longitudinal treatment data coupled with a comprehensive genomic characterization of cancers with a declared goal of decreasing the cancer burden and disparities across a wide spectrum of diseases and communities.

Assessment of Chinese urban land-use efficiency (SDG11.3.1) utilizing high-precision urban built-up area data

Assessment of SDG11.3.1 indicator of the United Nations Sustainable Development Goals (SDGs) is a valuable tool for policymakers in urban planning. This study aims to enhance the accuracy of the SDG11.3.1 evaluation and explore the impact of varying precision levels in urban built-up area on the indicator's assessment outcomes. We developed an algorithm to generate accurate urban built-up area data products based on China's Geographical Condition Monitoring data with a 2 m resolution. The study evaluates urban land-use efficiency in China from 2015 to 2020 across different geographical units using both the research product and data derived from other studies utilizing medium and low-resolution imagery. The results indicate: (1) A significant improvement in the accuracy of our urban built-up area data, with the SDG11.3.1 evaluation results demonstrating a more precise reflection of spatiotemporal characteristics. The indicator shows a positive correlation with the accuracy level of the built-up area data; (2) From 2015 to 2020, Chinese prefecture-level cities have undergone faster urbanization in terms of land expansion relative to population growth, leading to less optimal land resource utilization. Only in extra-large cities does urban population growth show a relatively balanced pattern. However, urban population growth in other regions and cities of various sizes lags behind land urbanization. Notably, Northeast China and small to medium cities encounter significant challenges in urban population growth. Our comprehensive framework for evaluating SDG11.3.1 with high-precision urban built-up area data can be adapted to different national regions, yielding more accurate SDG11.3.1 outcomes. Our urban area and built-up area data products provide crucial inputs for calculating at least four indicators related to SDG11.

Superposition Rate: A Developed Approach for Analyzing Production Data in Tight Gas Reservoirs

The paper examines the application of various methods to analyze production data in which the superposition principle being one of them. This principle is divided into two main methods: Superposition rate and Superposition time. Both are designed to deal with multiple flow rates within production data. The Superposition time converts the production time into constant values and uses the final flow rate value. On the other hand Superposition rate consolidates multiple rates into a single flow rate during production time. This study focuses on analysis of production data using a developed method based on superposition-rate approach. This method converts variables such as flow rate and pressure into constant values, where each flow regime has its own technique by which superposition-rate is utilized. In addition, comparison between conventional and unconventional reservoirs is involved, particularly emphasizing the benefits and drawbacks of tight gas reservoirs.Various types of superposition methods with their functions were discussed. Among them, a workflow part includes a case study of a dominated flow regime used for a hydraulic fractured single well. The workflow part of this study includes a case study of hydraulically fractured well under a dominant flow regime. Results comparing multiple rates method with super position-rate method indicate that the latter has better accuracy with (R) coefficient of (0.96). Case study on fracture reservoir also shows further validated of superposition-rate by accurately estimating drainage volume.

Analysis of Regional Economic Resilience through Comparative Advantages of Sectoral Growth

The objective of this study is to analyze the economic resilience of regions during the COVID-19 period that affected the Indonesian regional economy. Economic resilience can be identified by examining the development of leading sectors in the regional economy. This study uses secondary data in the form of time series using Gross Regional Domestic Product (GRDP) data obtained from the Central Bureau of Statistics of Batu City, the Central Bureau of Statistics of Malang City, and the Central Bureau of Statistics of Kediri City from 2018 to 2021. All three cities are located in East Java Province, Indonesia. The analysis in this study involves using three methods: Location Quotient, Shift Share, and Klassen typology. The analysis results reveal that Batu City and Malang City show strong competitiveness and comparative advantages. On the other hand, Kediri City demonstrates relatively low competitiveness and comparative advantage, which can be attributed to the impact of the COVID-19 shock. Additionally, research results show that the sectors in Malang City have experienced rapid growth, with all sectors developing faster than in other regions. In contrast, the sectors contributing to Batu and Kediri’s Gross Regional Domestic Product (GRDP) have experienced delayed development. The results of this study confirm that regional economic resilience still exists through the development of sectoral businesses that contribute to the overall value of Gross Regional Domestic Product in various regencies and cities within East Java Province. Keywords: basic sector, resilience, shift share, location quotient, regional economy

Employing Reflexivity in Sexuality Socialisation Research: A Methodological Contribution from Psychosocial Studies

In this paper, we describe and demonstrate the value of adopting a psychosocial methodology to explore unique sexual socialisation experiences emphasising the role of reflexivity. Psychosocial methodology emerges from Psychosocial Studies, a “transdisciplinary” area interested in phenomena from “both” a social and personal perspective and in this paper is employed to investigate how sexual socialisation is shaped by psychological processes “and” social relations, and how these can be “thought together” (Frosh &amp; Vyrgioti, 2022). Psychosocial data analytic strategies involve applying narrative and discursive psychology alongside psychoanalytic concepts to understand the possible reasons for a participant’s investment in particular discourses, understanding these investments as serving unique unconscious defensive purposes, alongside social functions. To illustrate this, we use data from a Free Association Narrative Interview with an isiXhosa-speaking “Black” socioeconomically disadvantaged woman in South Africa about her experiences of sexuality socialisation within her sister-sister relationship. We show how a psychosocial emphasis traverses traditional boundaries between discourse and affect, talk and experience, researcher and researched, moving across disciplinary spaces. Furthermore, we pay attention to what is frequently considered the background of research – the study context; the emotional quality of the interview encounter between the researcher and participant; the researchers’ relationship with one another and their contribution to both the data production and analysis. This emphasis on reflexivity in psychosocial methodology is consistent with the political and philosophical position of Psychosocial Studies that is critical of the reification of disciplinary knowledge.

A Coupled Model of Multiscaled Creep Deformation and Gas Flow for Predicting Gas Depletion Characteristics of Shale Reservoir at the Field Scale

The viscoelastic behavior of shale reservoirs indeed impacts permeability evolution and further gas flow characteristics, which have been experimentally and numerically investigated. However, its impact on the gas depletion profile at the field scale has seldom been addressed. To compensate for this deficiency, we propose a multiscaled viscoelasticity constitutive model, and furthermore, a full reservoir deformation–fluid flow coupled model is formed under the frame of the classical triple-porosity approach. In the proposed approach, a novel friction-based creep model comprising two distinct series of parameters is developed to generate the strain–time profiles for hydraulic fracture and natural fracture systems. Specifically, an equation considering the long-term deformation of hydraulic fracture, represented by the softness of Young’s modulus, is proposed to describe the conductivity evolution of hydraulic fractures. In addition, an effective strain permeability model is employed to replicate the permeability evolution of a natural fracture system considering viscoelasticity. The coupled model was implemented and solved within the framework of COMSOL Multiphysics (Version 5.4). The proposed model was first verified using a series of gas production data collected from the Barnett shale, resulting in good fitting results. Subsequently, a numerical analysis was conducted to investigate the impacts of the newly proposed parameters on the production process. The transient creep stage significantly affects the initial permeability, and its contribution to the permeability evolution remains invariable. Conversely, the second stage controls the long-term permeability evolution, with its dominant role increasing over time. Creep deformation lowers the gas flow rate, and hydraulic fracturing plays a predominant role in the early term, as the viscoelastic behavior of the natural fracture system substantially impacts the long-term gas flow rate. A higher in situ stress and greater formation depth result in significant creep deformation and, therefore, a lower gas flow rate. This work provides a new tool for estimating long-term gas flow rates at the field scale.