Carbonate Gas Reservoir Research Articles

Analysis of Reservoir Damage Mechanism and Prevention Measures for Deep Carbonate Gas Reservoir with High Sulfur Content

Analysis of Reservoir Damage Mechanism and Prevention Measures for Deep Carbonate Gas Reservoir with High Sulfur Content

Reservoir Body Development Characteristics in Deep Carbonate Gas Reservoirs: A Case Study of the Fourth Member of the Dengying Formation, Anyue Gas Field

Reservoir Body Development Characteristics in Deep Carbonate Gas Reservoirs: A Case Study of the Fourth Member of the Dengying Formation, Anyue Gas Field

Pore-Scale Numerical Simulation of Acid-Rock Reaction Processes in Carbonate Reservoirs.

In the process of acidizing carbonate reservoirs, dissolution is employed for reservoir modification to enhance recovery rates. This study establishes a numerical model at the pore scale for acid-rock reaction flow based on a microscopic continuum medium model, integrating phase-field theory and component transport models. Subsequently, the results of the Darcy-Brinkman-Stokes model are compared to those of the arbitrary Lagrange-Euler method to validate the accuracy of the model. Finally, the flow behavior of the acid solution at the pore scale and the complex dissolution mechanisms in carbonate reservoirs are analyzed. The research indicates that the microscopic pore-scale dissolution in carbonate reservoirs mainly manifests as five dissolution modes: uniform dissolution, compact dissolution, conical wormholes, dominant wormhole, and ramified wormholes. Different distributions of microfractures will alter the flow state of the acid solution and the rock-acid reaction process within the pores. Once the wormhole breakthrough occurs, there is an increased probability of acid flow through the wormhole to the outlet, leading to a decrease in the effectiveness of the acidizing carbonate reservoirs. A proper understanding of pore-scale acid-rock reaction laws is of great significance for the development of carbonate oil and gas reservoirs.

Experimental Study on Gas Production Capacity of Composite Reservoir Depletion in Deep Carbonate Gas Reservoirs

Deep carbonate gas reservoirs exhibit diverse reservoir types and complex seepage patterns. To study the gas production capabilities of different composite reservoir types, we classified the reservoirs of the fourth member of the Dengying Formation in the Anyue Gas Field into high-quality reservoirs (HRs) and poor-quality reservoirs (PRs) based on high-pressure mercury injection (HPMI) experiment results. By varying the differential pressure of the depletion experiment and the connection method, as well as the permeability and water saturation of the composite core, the effects of well location deployment, permeability ratio of the high-quality reservoir and poor-quality reservoir (PRHPR), gas well production pressure difference (GWPPD), and water saturation on the depletion gas production characteristics of the composite reservoir were studied. The research results show that (1) deploying wells on HR enables high gas production rates and ultimate recovery rates; (2) only when the PRHPR falls within a reasonable range (21.88–43.19) can the “dynamic recharge” capability of PR and the high permeability of HR be coordinated to achieve high gas recovery rates; (3) a GWPPD of 3 MPa is optimal, resulting in fast gas production rates and high ultimate recovery rates for PR; (4) high water saturation (≥50%) leads to premature water breakthrough at the well bottom, decreased gas production rate, and sealing of HR and PR reserves by formation water. Combining experimental results with field production data is our next research focus. Our future research focus will be on integrating experimental results with field production data to provide solid theoretical support for the efficient development of this type of gas reservoir.

An Integrated Numerical Study of CO2 Storage in Carbonate Gas Reservoirs with Chemical Interaction between CO2, Brine, and Carbonate Rock Matrix

In light of the burgeoning interest in mitigating anthropogenic CO2 emissions, carbonate reservoirs have emerged as promising sequestration sites due to their substantial storage potentials. However, the complexity of CO2 storage in carbonate reservoirs exceeds that in conventional sandstone reservoirs, predominantly due to the rapid interactions occurring between the injected CO2, brine, and carbonate rock matrix. In this study, a numerical model integrated with the chemical CO2–brine–rock matrix interaction was developed to analyze the carbonate rock dissolution process and the physical property variations of different carbonate gas reservoirs during the CO2 injection and sequestration process. More specifically, a total of twenty scenarios were simulated to examine the effects of lithology, pore size, pore–throat structures, and CO2 injection rate on carbonate rock matrix dissolution and reservoir property variation. Calcite is significantly easier and quicker to react with CO2-solvated brine than dolomite; as a result, limestones exhibit an expedited rock dissolution and pore volume increase, along with a slower pressure buildup in comparison to dolomites. Also, the carbonate reservoir with a smaller pore size has a higher rock dissolution rate than one with a larger pore size. Furthermore, the simulation results show injected CO2 can modify the pore-dominant carbonate reservoir to a more pronounced extent than the fracture-dominant carbonate reservoir. Lastly, the carbonate rock dissolution is more obvious at a lower CO2 injection rate. The insights derived from this research aid evaluations related to CO2 injectivity, storage capacity, and reservoir integrity, thereby paving the way for environmentally and structurally sound carbon sequestration strategies.

Deep carbonate gas reservoir sweet spot identification with seismic data based on dual-factor control of sedimentary facies and fault system

Deep carbonate reservoirs are attractive targets for gas development. These reservoirs are deeply buried, and commonly possess strong heterogeneity and poor seismic data quality, making the identification of favorable production areas (“sweet spots”) challenging. Furthermore, sedimentary facies and fault systems markedly impact reservoir quality, and identifying these features in seismic data is also crucial for sweet spot identification. To solve these problems, we propose a dual-factor-controlled sweet spot identification method with two steps. First, sedimentary facies and faults are identified separately at different seismic scales using different attributes by the steerable pyramid (SP) method. The SP method decomposes the original seismic data into high-frequency and low-frequency data. The amplitude attributes from high-frequency data are used to identify sedimentary facies, and coherence attributes based on low-frequency data are used to characterize the fault systems. Second, after separately identifying the sedimentary facies and faults, the two attribute volumes are merged together to identify reservoir sweet spots. The results are verified by using well production data. The results of a field study in the Dengying Formation deep carbonate reservoir in the central Sichuan Basin, China, indicate that reservoir sweet spots are primarily developed in ideal sedimentary facies along strike-slip fault systems. Sedimentary facies generally control the type and distribution of reservoirs, whereas strike-slip fault systems control the migration and accumulation of gas. In addition, the fault systems serve as karst channels that further improve the reservoir properties. The proposed dual-factor method might help to maximize exploration potential in deep carbonate reservoirs with similar settings.

Experimental Study on Edge Water Invasion of Strongly Heterogeneous Carbonate Gas Reservoirs Based on NMR Technology

Controlling the extent of water invasion in the reservoir and mitigating its detrimental effects on gas well production and natural gas recovery have long been a challenging task in the efficient development of strongly heterogeneous edge water gas reservoirs. To elucidate the edge water invasion mechanism of strongly heterogeneous carbonate gas reservoirs, this study investigates the pore throat characteristics and fluid mobility from both qualitative and quantitative aspects, leveraging natural core observations, cast thin sections, scanning electron microscopy (SEM), and nuclear magnetic resonance (NMR) tests with centrifuge experiments. A core-scale edge water invasion simulation experiment was conducted under online NMR monitoring to examine the dynamic gas production characteristics of the three types of reservoirs during the water invasion process and to elucidate the formation mechanism and distribution pattern of water-sealed gas. Research findings indicate that carbonate reservoirs typically exhibit a diverse range of pore types, including various types of fractures and cavities. Fractures significantly enhance reservoir connectivity, thereby increasing fluid mobility, but also lead to strong non-uniform water invasion. In contrast, cavities substantially improve the storage capacity of the reservoir and can retard the advancement of the water invasion front, thereby alleviating the adverse effects of water invasion. The ultimate recovery rates of fracture-type, cavity-type, and fracture-cavity cores in the water invasion simulation experiment were 29.81%, 64.87%, and 53.03%, respectively. Premature water breakthroughs in the reservoir can result in a large number of gases in matrix pores and even cavities being sealed by formation water, rendering them unrecoverable, which seriously impacts the gas recovery rate of the reservoir.

CO2 injection-based enhanced methane recovery from carbonate gas reservoirs via deep learning

CO2 injection is a promising technology for enhancing gas recovery (CO2-EGR) that concomitantly reduces carbon emissions and aids the energy transition, although it has not yet been applied commercially at the field scale. We develop an innovative workflow using raw data to provide an effective approach in evaluating CH4 recovery during CO2-EGR. A well-calibrated three-dimensional geological model is generated and validated using actual field data—achieving a robust alignment between history and simulation. We visualize the spread of the CO2 plume and quantitatively evaluate the dynamic productivity to the single gas well. We use three deep learning algorithms to predict the time histories of CO2 rate and CH4 recovery and provide feedback on production wells across various injection systems. The results indicate that CO2 injection can enhance CH4 recovery in water-bearing gas reservoirs—CH4 recovery increases with injection rate escalating. Specifically, the increased injection rate diminishes CO2 breakthrough time while concurrently expanding the swept area. The increased injection rate reduces CO2 breakthrough time and increases the swept area. Deep learning algorithms exhibit superior predictive performance, with the gated recurrent unit model being the most reliable and fastest among the three algorithms, particularly when accommodating injection and production time series, as evidenced by its smallest values for evaluation metrics. This study provides an efficient method for predicting the dynamic productivity before and after CO2 injection, which exhibits a speedup that is 3–4 orders of magnitudes higher than traditional numerical simulation. Such models show promise in advancing the practical application of CO2-EGR technology in gas reservoir development.

Flowback Additive for Acidizing Fluid to Stimulate Carbonate Gas Reservoirs

Summary Acidizing of oil- and gas-bearing carbonate reservoirs is generally undertaken by using strong mineral acids such as hydrochloric acid (HCl) to enhance permeability. One of the major challenges associated with HCl injection is tuning the reactivity profile to favor the transport of live acid deep into the reservoir while achieving a minimum rock face dissolution. The mineral acid is therefore emulsified in a hydrocarbon phase (e.g., diesel) to retard its reactivity with the rock matrix. The use of emulsified acid is hindered by several limitations such as low emulsion stability at high temperatures, pumping limitations due to high viscosity, the potential of formation damage, and cumbersome mixing procedures at the field scale. In addition, the brines formed as a result of this reaction can be difficult to produce due to higher density and capillary pressures, unfavorable wettability, and low formation pressure. Here, we report on the development of dual-purpose additives that were specifically designed to enhance the recovery of high-density brines and retard the acid/rock reactivity upon addition to the stimulation treatment. Accordingly, seven new additives with fluid flowback properties were developed for use in a single-phase acidizing fluid consisting of HCl (15 wt% and 28 wt%) with the required additives, such as corrosion inhibitor and intensifier, and H2S scavenger. The flowback enhancers (FBEs) were formulated from a blend of water, ester or terpene solvents, alcohols, and surfactants to form optically clear nano- and microemulsions. Surfactant selection was driven by the need to exhibit demulsification properties with condensate, high chemical and thermal stability, compatibility in strongly acidic media, and high-density brines under harsh reservoir conditions. To assess the FBE performance in acidizing formulations (i.e., to serve as both an FBE and retarder), screening studies consisting of static rock dissolution tests and surface tension measurements were performed to downselect FBEs suitable for this application. This was coupled with brine displacement tests in addition to compatibility and stability studies. FBEs that demonstrated superior performance were then selected for further evaluation under reservoir conditions [i.e., core flow matrix acidizing to measure regained permeability and computed tomography (CT) scan for analyzing the wormhole propagation]. The droplet size of the as-prepared nano- and microemulsions was found to be between 10 nm and 850 nm. The FBEs formulated in this study were found to prevent emulsion formation in the presence of condensate and demonstrated remarkable chemical and thermal stability in concentrated acid at temperatures up to 300°F for a duration of up to 24 hours, as confirmed by the consistent low surface tension values (21–29 mN/m). With regard to fluid displacement, column tests performed under ambient conditions revealed quick brine displacement with recovery exceeding 75 vol% in comparison with 16 vol% in the absence of the FBE. Interestingly, the addition of a select FBE from this study to 28 wt% HCl was found to retard the reaction of carbonate dissolution at room temperature. This led us to assess the performance under reservoir conditions utilizing core flow testing. Accordingly, the addition of FBE-F to 28 wt% HCl led to an improvement in permeability by up to 267% as compared with 15% without FBE added. These results are further supported by the CT scan images of the acidized cores, which revealed the formation of a deeper wormhole in the presence of a select FBE.

Deep learning algorithm-enabled sediment characterization techniques to determination of water saturation for tight gas carbonate reservoirs in Bohai Bay Basin, China

Understanding water saturation levels in tight gas carbonate reservoirs is vital for optimizing hydrocarbon production and mitigating challenges such as reduced permeability due to water saturation (Sw) and pore throat blockages, given its critical role in managing capillary pressure in water drive mechanisms reservoirs. Traditional sediment characterization methods such as core analysis, are often costly, invasive, and lack comprehensive spatial information. In recent years, several classical machine learning models have been developed to address these shortcomings. Traditional machine learning methods utilized in reservoir characterization encounter various challenges, including the ability to capture intricate relationships, potential overfitting, and handling extensive, multi-dimensional datasets. Moreover, these methods often face difficulties in dealing with temporal dependencies and subtle patterns within geological formations, particularly evident in heterogeneous carbonate reservoirs. Consequently, despite technological advancements, enhancing the reliability, interpretability, and applicability of predictive models remains imperative for effectively characterizing tight gas carbonate reservoirs. This study employs a novel data-driven strategy to prediction of water saturation in tight gas reservoir powered by three recurrent neural network type deep/shallow learning algorithms—Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), K-nearest neighbor (KNN) and Decision tree (DT)—customized to accurately forecast sequential sedimentary structure data. These models, optimized using Adam's optimizer algorithm, demonstrated impressive performance in predicting water saturation levels using conventional petrophysical data. Particularly, the GRU model stood out, achieving remarkable accuracy (an R-squared value of 0.9973) with minimal errors (RMSE of 0.0198) compared to LSTM, RNN, SVM, KNN and, DT algorithms, thus showcasing its proficiency in processing extensive datasets and effectively identifying patterns. By achieving unprecedented accuracy levels, this study not only enhances the understanding of sediment properties and fluid saturation dynamics but also offers practical implications for reservoir management and hydrocarbon exploration in complex geological settings. These insights pave the way for more reliable and efficient decision-making processes, thereby advancing the forefront of reservoir engineering and petroleum geoscience.

A New Evaluation Method of Recoverable Reserves and Its Application in Carbonate Gas Reservoirs

A New Evaluation Method of Recoverable Reserves and Its Application in Carbonate Gas Reservoirs

Effect of pore structure characteristics on gas-water seepage behaviour in deep carbonate gas reservoirs

The study of gas-water two-phase flow behavior has always been a focus and a difficult problem in the field of oil and gas seepage, and it is related to the formulation of reasonable development plans for oil and gas reservoirs. Deep carbonate gas reservoirs (depth>4500m) develop cross-scale porous media, which have extremely strong heterogeneity and very complex seepage characteristics. To the best of our knowledge, there has been no systematic study on the two-phase flow patterns of gas and water in deep carbonate gas reservoirs. This paper uses cast thin sections, scanning electron microscopy (SEM) and nuclear magnetic resonance (NMR) to study the influence of carbonate rock pore structure characteristics on gas storage and seepage capabilities. The results show that the existence of cavities enhances the gas storage capacity of the reservoir and improves the seepage capacity of pore-throat space near the cavity. Fractures can effectively communicate with isolated cavities, enhance reservoir connectivity, and thereby significantly improve the overall seepage capacity of the reservoir. The centrifugal technique was combined with NMR to study the movable fluid characteristics of carbonate reservoirs. The critical flow throat radius and movable fluid saturation of the reservoir reveal that the degree of development of cavities and fractures determines the mobility of the fluid in the reservoir. The fluid mobility in the fracture-cavity-type reservoir is the strongest, while the fluid mobility in the pore-type reservoir is the weakest. Analysis of the normalized curve of gas-water relative permeability under high temperature and pressure reveals that formation water preferentially enters connected pores with low seepage resistance, and the strong storage capacity of cavities can effectively delay the non-uniform intrusion of formation water. The results of water invasion simulation experiments show that the water invasion phenomenon can improve the gas production rate and recovery rate of pore-type reservoirs. However, the formation water flow and channeling phenomena caused by reservoir heterogeneity will cause a large amount of gas to be sealed in the pore space of cavity-type and fracture-cavity-type reservoirs, reducing the recovery rate and economic benefits of these reservoirs. These findings further provide solid fundamentals for efficient and effective exploitation of deep carbonate gas reservoirs.

Sources of natural gas in the Middle-Permian carbonate succession in the central-northern Sichuan Basin, Southwest China

Natural gas-source correlation is a challenging aspect of natural gas exploration in deep strata. The Middle-Permian carbonate succession in the Sichuan Basin, southwest China, particularly in the Maokou Formation, is a hotspot for natural gas exploration. However, determining the natural gas source in the Maokou Formation is challenging because of the high maturity and complexity of potential source rocks. In this study, we analyzed the genesis and sources of natural gas in the central-northern Sichuan Basin while considering the geological conditions and systematically utilizing the gas components, stable and rare-gas isotopes, and solid bitumen biomarkers. The results indicate that (1) the natural gas in the northern Sichuan Basin comprises a mixture originating from the source rocks of the Maokou and Wujiaping formations; (2) the Qiongzhusi Formation is the major source of natural gas in the north slope (and the Maokou Formation source rock could be used as an important supplement); (3) the natural gas in the Moxi-Longnvsi structure comprises a mixture originating from the source rocks of the Qiongzhusi and Maokou formations. From south to north and west to east, the major source rocks of the gas reservoirs changed from belonging to the Qiongzhusi Formation to the Maokou and Wujiaping formations. This study is the first to combine multiple geochemical methods and consider different geological settings to identify the gas source of a deep carbonate gas reservoir. Our study provides a reliable method for determining the natural gas-source correlation of gas reservoirs that contain highly mature and complex gas sources.

Factors Controlling Productivity – A Case Study on the Qixia Formation of the Shuangyushi Area, Sichuan Basin

The gas reservoir with high temperature and high pressure (HTHP) over 8,000 m is one of the carbonate gas reservoirs discovered in recent years in the Qixia Formation of the Shuangyushi area, NW Sichuan Basin, and the strong heterogeneity makes it difficult to deploy high-yield wells. The relationship between geology and productivity is given by using the research results of reservoir evaluation and statistical analysis of the dynamic analysis of trial production in the last 4 years in this paper. Then, the evaluation index system of high productivity controlling factors in the gas reservoir is given by using the fuzzy clustering analysis method. Finally, the relative importance of each index is quantitatively analyzed and evaluated by using the analytic hierarchy process to determine the key controlling factors affecting productivity. The evaluation results are highly consistent with the current characteristics of high-yield wells. The productivity of the newly completed Well 008 and Well 107 outside the trial mining area exceeded 1 million m3/day. The result shows that the high productivity of ultradeep gas wells in the Qixia Formation is mainly controlled by the degree of dolomitization, the distribution of high-energy shoal-mound complexes, and the degree of fracture development. It can be used to guide the deployment of productive wells in the southern area of the Shuangyushi-producing test area.

An enhancement in the petrophysical evaluation in a vuggy carbonate gas reservoir by integrating the core data and empirical methods, Zagros basin, south of Iran

The presence of vuggy pore types poses challenges in accurately assessing effective porosity. This study focuses on the significant scientific issue of improving petrophysical evaluation in vuggy carbonate gas reservoirs. The Kangan Formation is one of the main gas reservoir formations in the southern Zagros region, Iran. The main objective of the current research is to distinguish and exclude the influence of vuggy pore types from effective porosity in the reservoir pay zones of the Kangan reservoir.In the current research, a combination of full suite logs, image logs, core analysis, and thin section studies was employed. The image logs illustrate that vuggy porosity is abundant in the Kangan Formation and these results are confirmed by the available core thin sections, specifically in Zone Kangan_B; Additionally, the cross plots of compressional velocity versus bulk density and total porosity, as a part of rock physics study, indicate the characteristics of the vuggy reservoir. Two methods are utilized to quantify vuggy porosity. The first method, the Velocity Deviation Log (VDL) approach, identifies various available pore types, especially vugs. The second method is a newly proposed approach that can exclude vuggy porosity from the computed effective porosity. In this novel approach, a variable porosity exponent (m) is derived by adopting the Lucia equation to exclude vuggy porosity from the effective porosity computations. Thus, petrophysical evaluation can be implemented based on the constant and variable “m”. Comparing both petrophysical results, it is evident that the amounts of effective porosity and water saturation are modified in the vuggy-bearing intervals. Applying the proposed approach will improve the accuracy of petrophysical properties and lead to the proper calculation of the hydrocarbon volume in the carbonate reservoir rocks containing isolated vugs, particularly in gas-bearing reservoirs where conventional logs are affected by gas contents.

Technology Focus: History Matching and Forecasting (April 2024)

This year’s history matching and forecasting selections cover topics that include a deep-learning framework to forecast spatial/temporal dynamics of CO2 mineralization in reactive rocks, productivity decline analysis using coupled full-field and near-wellbore poromechanics modeling, and a reservoir graph network model for multiwell forecasting in unconventional reservoirs. In paper SPE 216998, the authors present a deep-learning methodology to create surrogate models capable of predicting the precipitation of minerals during CO2 injection into deep saline aquifers. The authors state that this approach allows for the rapid assessment of the individual contributions of each mineral in the CO2 mineralization process. A coupling scheme of a full-field reservoir model and a near-wellbore poromechanics model, its application on productivity index (PI) decline analysis for a deepwater reservoir, and the evaluation of the drawdown effect on well productivity is the subject of paper SPE 212200. According to the authors, the coupling scheme captured geomechanical damages, predicted PI trends with different drawdowns, and enabled identification of safe drawdown limits for operational decisions. The authors of paper URTeC 3855422 present a hybrid approach that combines physics with data-driven approaches for forecasting the performance of unconventional wells under codevelopment. As per the authors, the reservoir graph network model, which describes the drainage volume of an unconventional well using a set of 1D gridblocks parameterized by pore volume and transmissibility, provides an efficient way to model, history-match, and forecast the performance of unconventional wells by reducing the system complexity while maintaining the fundamental physics. Recommended additional reading at OnePetro: www.onepetro.org. URTeC 3862949 A Robust Workflow To Characterize Uncertainties of a Horizontal Well Pad: Multiwell History Matching for Unconventional Reservoirs by Chuxi Liu, The University of Texas at Austin, et al. SPE 212668 Application of Simulation Opportunity Index and Assisted History Matching for Fishbone Completion Strategy in the Existing Single Horizontal Well in a Tight Gas Carbonate Reservoir by Bondan Bernadi, ADNOC, et al. SPE 212723 Automated Production Forecasting Using a Novel Machine-Learning-Based Approach by Kaustubh Shrivastava, SLB, et al.

Microscopic occurrence of primary water in carbonate gas reservoirs with fractures and vugs and its effect on water invasion mechanism

The structure of carbonate gas reservoirs has multiple pore-fracture cave media, and the gas–water flow mechanism is complex. Computed tomography (CT) and nuclear magnetic resonance (NMR) were used to explore the pore structure differences and collocation relationships of different small-scale carbonate cores with fractures and holes in the Longwangmiao Formation. Based on the concept of fractal dimensions, a quantitative method for water film occurrence was established, combined with surface wetting physics and mechanics. The formation of primary irreducible water and the influence mechanism of the water film on water invasion flow were clarified, the mechanical control mechanism of gas–water flow was analysed from the microelement structure, and the difference in water invasion in different pore structures was proven. This study showed that the bound water in carbonate rocks is controlled by micromechanics and occurs in the reservoir space through four mechanisms. Under the action of intermolecular forces and linear tension, a small amount of water film is formed as a movable pore surface, a small amount of pore edge space exists in the form of a corner because of the uneven rough pore surface, and a small amount exists in pores with pore diameters less than the critical pore diameter to form filling. Numerous pores occurred in isolated pores coordinated only by pores smaller than the critical pore size. Furthermore, the flow of the intrusive water phase is controlled by micromechanics such as the displacement force, viscous force of the water film layer on the pore wall, and the capillary force, which mainly occurs in connected pores and fractures larger than the “critical pore diameter of water invasion.” The “critical pore diameter” of the Longwangmiao formation ranges from 2.2 μm to 3.4 μm, and only the pores coordinated by small pores below the “critical pore diameter of water invasion” cannot be affected by the intrusive water phase due to the action of “pore resistance.” To form residual gas.

Evaluation of the effectiveness of mixing hydrochloric acid and organic acid as stimulation fluids in a tight gas carbonate reservoir

AbstractMatrix acidizing is a widely used technique in carbonate formations to improve production and injection rates by restoring or improving permeability. This study evaluates the stimulation efficacy of organic and inorganic acid combinations in the Sarajeh carbonate formation, with a focus on improving permeability within this potential tight gas formation. Through solubility and continuous flow tests, a mixture of HCl 7.5% + HAc 2.5% was found to significantly outperform the traditional HCl 15% solution. The HCl 7.5% + HAc 2.5% mixture had a solubility rate of 59.27% and improved permeability by 4.4 times compared to HCl 15%, indicating a higher dissolving capacity. The results of continuous flow tests showed that the mixed acid had the best pore volume to breakthrough (PVBt) value of 2.574 at a controlled injection rate of 2 cc/min. This study demonstrates the superior performance of a lower concentration HCl‐HAc acid formulation in matrix acidizing carbonate formations and confirms its potential as a more effective alternative to high concentration HCl treatments for the Sarajeh reservoir.

Production forecasting methods for different types of gas reservoirs

Hydrocarbon production in oil and gas fields generally progresses through stages of production ramp-up, plateau (peak), and decline during field development, with the whole process primarily modeled and forecasted using lifecycle models. SINOPEC's conventional gas reservoirs are dominated by carbonates, low-permeability tight sandstone, condensate, volcanic rocks, and medium-to-high-permeability sandstone. This study identifies the optimal production forecasting models by comparing the fitting coefficients of different models and calculating the relative errors in technically recoverable reserves. To improve forecast precision, it suggests substituting exponential smoothing method-derived predictions for anomalous data caused by subjective influences like market dynamics and maintenance activities. The preferred models for carbonate gas reservoir production forecasts are the generalized Weng's, Beta, Class-I generalized mathematical, and Hu-Chen models. The Vapor pressure and Beta models are optimal for forecasting the annual productivity of wells (APW) from gas-bearing low-permeability tight sandstone reservoirs. The Wang-Li, Beta, and Yu QT tb models are apt for moderate-to-small-reserves, single low-permeability tight sandstone gas reservoirs. The Rayleigh, Hu-Chen, and generalized Weng's models are suitable for condensate gas reservoirs. For medium-to-high-permeability sandstone gas reservoirs, the lognormal, generalized gamma, and Beta models are recommended.

A novel score system to evaluate carbonate reservoir combining microscale and macroscale parameters

The central Sichuan Basin, located in western China, holds great significance in terms of hydrocarbon production, especially in relation to complex carbonate reservoirs, notably the Qixia Formation of the Middle Permian epoch. However, the comprehensive evaluation of this geological formation presents considerable challenges due to complex lithology, limited availability of reservoir property data at various scales, inadequacies in data integration, and the absence of a reliable reservoir ranking system for development decision making. Previous studies primarily relying on conventional data at the reservoir level, such as well logs and geological information, have proven insufficient in accurately characterizing the reservoir. This is especially evident without precise lithological information and detailed knowledge of microscale properties, which are crucial for effective reservoir evaluation. To address these challenges, this study integrates advanced technologies like X-ray diffraction, micro-CT and scanning electron microscope (SEM) techniques in digital drill cutting analysis at the microscale. A novel scoring system has been developed using a prominent component analysis (PCA) approach and an expert scoring system, which incorporates existing conventional well log data and digital cutting analysis. This scoring system has been validated using actual production data, thus establishing a robust evaluation methodology for assessing the exploration potential and optimizing development strategies for carbonate gas reservoirs in the Qixia Formation. The innovative approach, which incorporates parameters at both micro- and macroscales, holds promising potential in facilitating future exploration and development efforts.