Journal of Petroleum Exploration and Production Technology最新文献

In acid fracturing, acid is injected into artificial fracture to dissolve the rock, and the rock surface is shaped into the non-uniform morphology. This non-uniform morphology is the main cause of fracture conductivity after acid etching. The effects of acid type, flow rate, contact time and rock type on the non-uniform morphology of acid-etched rock surface have been studied. However, when the direction of acid flow is determined, the difference of dissolution characteristics in different directions of rock surface is not investigated. In this paper, acid rock reaction experiment and analysis of rock surface morphology after acid etching are carried out by using rock samples with flat surface. The difference of morphological characteristic parameters of rock surface in different directions under the condition of acid flow is discussed. It was found that the mean drop height, drop height range and roughness coefficient increase with the increase of acid rock reaction rate on flat rock surface. The morphological characteristic parameters of acid-etched rock face of limestone are greater than those of dolomite, which is conducive to forming acid-etched channels. According to the statistics of all experimental data, the larger average drop height of rock surface is distributed in the direction of parallel acid flow, the larger drop height range is distributed in the direction of vertical acid flow and other directions, and the larger roughness coefficient is distributed in other directions.

During the development of a field, many fluid samples are taken from wells. Selecting a robust fluid sample as the reservoir representative helps to have a better field characterization, reliable reservoir simulation, valid production forecast, efficient well placement and finally achieving optimized ultimate recovery. First, this paper aims to detect and separate the samples that have been collected under poor conditions or analyzed in a non-standard way. Moreover, it introduces a novel ranking method to score the samples based on the amount of coordination with other fluid samples in the region. The dataset includes 136 fluid samples from five reservoirs in Iranian fields, each of them consisting of 21 key parameters. Five acknowledged machine learning based anomaly detection techniques are implemented to compare fluid samples and detect those whose results deviate from others, indicating non-standard samples. To ensure the proper detection of outlier data, the results are compared with the traditional validation method of gas-oil ratio estimation. All five outlier detection methods demonstrate acceptable performance with average accuracy of 79% compared to traditional validation. Furthermore, the fluid samples with the highest scores in scoring-based algorithms are introduced as the best reservoir’s representative fluid. Finally, fuzzy logic is used to obtain a final score for each sample, taking the results of the six methods as input and ranking the samples based on their output score. The study confirms the robustness of the novel approach for fluid validation using outlier detection techniques and the value of machine learning and fuzzy logic for sample ranking, excelling in considering all critical fluid parameters simultaneously over traditional methods.

Sand production poses a substantial challenge in the oil and gas field, particularly in formations lacking the requisite strength to withstand pressure differentials during extraction. Many wells produce much less than their capacity due to the need to mitigate sand production and prevent well completion and wellhead erosion. The choice of sand control method depends on various factors, such as reservoir conditions, operational methods, and economic considerations, encompassing both mechanical and chemical approaches. In response to this challenge, this study investigates the application of a novel polymer nanofluid for chemical consolidation in clay-rich sandstone reservoirs, an area of exploration yet to be fully tapped. The research aims to assess the potential of polymer nanofluids as a promising solution for sand control in clay-rich reservoirs, with the overarching goal of bolstering well productivity and mitigating the adverse impacts of sand production. Conducted at a laboratory scale, the experiments involved the injection of 1 Pore Volume of consolidation fluid into sandstone cores with 15% and 30% clay content. Subsequently, the cores were subjected to reservoir temperature and pressure conditions for a period of 24 h. The obtained results show a significant enhancement in compressive strength, exceeding 700 psi, facilitated by the polymer nanofluid. Furthermore, permeability restoration reached approximately 89%, a notable improvement compared to preceding studies. Moreover, the introduction of foam injection rendered the core surface water-wet, suggesting potential advantages for reservoir management. These findings illuminate the promise of polymer nanofluids as an effective tool for sand control in clay-rich sandstone reservoirs.

Previous attempts to classify flow units in Iranian carbonate reservoirs, based on porosity and permeability, have faced challenges in correlating the rock's pore size distribution with the capillary pressure profile. The innovation of this study highlights the role of clustering techniques, such as Discrete Rock Type, Probability, Global Hydraulic Element, and Winland's Standard Chart in enhancing the reservoir's rock categorization. These techniques are integrated with established flow unit classification methods. They include Lucia, FZI, FZI*, Winland R35, and the improved stratigraphic modified Lorenz plot. The research accurately links diverse pore geometries to characteristic capillary pressure profiles, addressing heterogeneity in intricate reservoirs. The findings indicate that clustering methods can identify specific flow units, but do not significantly improve their classification. The effectiveness of these techniques varies depending on the flow unit classification method employed. For instance, probability-based methods yield surpassing results for low-porosity rocks when utilizing the FZI* approach. The discrete technique generates the highest number of flow unit classes but provides the worst result. Not all clustering techniques reveal discernible advantages when integrated with the FZI method. In the second part, the study creatively suggests that rock classification can be achieved by concurrently clustering irreducible water saturation (SWIR) and porosity in unsuccessful flow unit delineation cases. The SWIR log was estimated by establishing a smart correlation between porosity and SWIR in the pay zone, where water saturation and SWIR match. Then, the estimated saturation was dispersed throughout the reservoir. Subsequently, the neural network technique was employed to cluster and propagate the three finalized flow units. This methodology is an effective recommendation when conventional flow unit methods fail. The study also investigates influential factors causing the failure of flow unit classification methods, including pore geometry, oil wettability, and saturation in heterogeneous reservoirs.

The water-shale interaction affect the shale structure, leading to wellbore instability and increasing drilling costs. The extent of structural changes within the shale can be determined non-destructively by analyzing its acoustic characteristics. Experiments were conducted to investigate the acoustic properties of shale from the Yanchang Formation in the Ordos Basin before and after exposure to brines of varying types, soaking times, and salinities. The study investigated the effects of brine type, soaking time, and salinity on shale’s acoustic properties, including changes in acoustic wave propagation speed, P/S wave velocity ratio, and both time-domain and frequency-domain amplitudes. The results indicate that although the type of brine has a limited impact on the water-shale interaction, KCl exhibits a significant inhibitory effect. However, the soaking time and the brine salinity have a significant impact on the acoustic properties of shale. As the soaking time increases, the decrease in wave velocity increases, the P/S wave velocity ratio increases, and the decrease in time-domain amplitude increases. The amplitude of the main frequency in the frequency domain signal also decreases with the increase of reaction time, which is consistent with the analysis results of the time domain signal. As the salinity of brine increases, the decrease in wave velocity decreases, the P/S wave velocity ratio decreases, and the decrease in time-domain amplitude decreases. The amplitude of the main frequency in the frequency domain signal also decreases with the increase of brine salinity, which is consistent with the analysis results of the time domain signal. This work establishes the relationship between water-shale interaction and acoustic characteristics, which can quantitatively evaluate the degree of interaction between water and shale without damaging shale. Furthermore, this research provides new insights and guidance for predicting drilling collapse cycles and optimizing drilling fluid compositions.

Effective water management in oil reservoirs is crucial for maximizing hydrocarbon recovery while minimizing environmental degradation. This paper investigates the potential of innovative chemical techniques to control water production in oil reservoirs and compares these methods with traditional mechanical strategies. By reviewing over 70 case studies extensively, this research provides a detailed evaluation of different approaches to managing water cut. The study reveals that chemical methods, particularly those utilizing advanced polymer-based gels, are generally more effective than mechanical techniques. These methods are especially advantageous in settings with high water cuts and geologically complex reservoirs. Chemical treatments provide greater adaptability and cost-efficiency, significantly reducing the environmental impact compared to mechanical approaches. The primary aim of this research is to analyze the sources of water cut and evaluate common water shutoff operations to enhance reservoir management based on specific challenges, reservoir characteristics, and economic considerations. Our findings suggest using a two-step strategy: starting with mechanical control methods and then applying chemical treatments specifically designed for the reservoir’s unique physical properties. This not only improves oil recovery rates but also enhances economic efficiency by extending the reservoirs’ lifespan. Future research should focus on developing cost-effective, environmentally friendly chemical solutions suitable for various geological settings. Such advancements could significantly refine water management practices in oil fields, leading to better economic and environmental outcomes.

Lost circulation and mud losses cause 10 to 20% of the cost of drilling operations under extreme pressure and temperature conditions. Therefore, this research introduces an integrated system for an automated lost circulation severity classification and mitigation system (ALCSCMS). This proposed system allows decision makers to reliability predict lost circulation severity (LCS) based on a few drilling drivers before starting drilling operations. The proposed system developed and compared a total of 11 ensemble machine learning (EML) based on collection 65,377 observations, the data was pre-processed, cleaned, and normalized to be filtered using factor analysis. For each generated algorithm, the proposed system performed Bayesian optimization to acquire the best possible results. As a result, the optimized random forests (RF) model algorithm was the optimal model for classification at 100% classification accuracy based on testing data set. Mitigation optimization model based on genetic algorithm has been incorporated to convert high severe classes into acceptable classes of lost circulation. The system classifies the LCS into 5 classes where the classes from 2 to 4 are converted to be class 0 or 1 to minimize lost circulation severity by optimizing the input parameters. Therefore, the proposed model is reliable to predict and mitigate lost circulation during drilling operations. The main drivers that served as LCS inputs were explained using the SHapley Additive exPlanations (SHAP) approach.

The practice of oil and gas exploration has confirmed the existence of recoverable shale oil resources in the Permian Lucaogou Formation on the northern edge of the Bogda Mountains. However, previous research on the development characteristics and main controlling factors of shale oil resources in this area is relatively limited. In order to elucidate the development characteristics and principal controlling factors of the shale deposit in the Lucaogou Formation, the characteristics, physical properties, diagenesis, and influencing factors of the shale ore were investigated utilising data derived from outcrop, drilling, seismic, and geochemical analysis. The findings indicate that the shale of the Lucaogou Formation is prevalent and extensive. The deposit’s lithology is predominantly composed of dark grey and grey-black mud shale, interspersed with thin layers of dark grey and grey-black sandy mud shale and dolomite mud shale. The most prevalent minerals are carbonate minerals, followed by feldspar and quartz, with a notable proportion of brittle minerals. The deposit is primarily composed of dissolution pores, bedding fractures, and structural fractures, with a porosity of 1.23–3.26% and permeability of 0.012–0.076 mD, which are characteristic of ultra-low porosity and ultra-low permeability deposits. Among the three deposit types, the sandstone type exhibits the most favourable physical properties, followed by the dolomite type and the shale type, which displays the least favourable properties. The shale of the Lucaogou Formation is currently in the middle diagenetic phase, which is characterised by compaction, cementation (carbonate cementation, mudstone cementation, pebble cementation), and dissolution. The destructive effect of compaction and cementation on the physical properties is counterbalanced by the constructive effect of dissolution. The diagenetic environment has gradually changed from an alkaline environment to a slightly alkaline, slightly acidic stage.

The current extension limit prediction model for offshore extended-reach well (ERW) in mudstone does not consider the formation collapse, which poses a huge risk to offshore drilling construction. To address this problem, this paper presents new open-hole extension limit prediction model for ERW. By considering formation collapse, rate of penetration (ROP), and annular pressure loss, the extension limit models during normal drilling and tripping were derived. The sensitivity of geological and engineering factors was evaluated by analyzing limits for wells 1H and 2H. The research results showed that: (1) The extension limit increases with the ROP and formation collapse duration, but decreases with the increase in mud weight, plastic viscosity, and flow rate. (2) In ERWs, the mud flow rate has a significant impact on the extension limit than the plastic viscosity of mud fluid. However, mud weight has the least impact compared to the two. (3) Considering various parameters, the predicted extension limit of well 1H, when the mud weight is 1.16 g/cm³ [9.67 ppg], is 1593.28 m [5225.96 ft] less than the limit when the collapse period is not considered.

In gas injection, which is one of the fascinating enhanced oil recovery techniques, the main mechanism involves decreasing interfacial tension (IFT). Although various mechanisms can affect the IFT of a system, in most experimental and numerical studies, condensation is considered the dominant mechanism among condensation-vaporization and vaporization. Investigating the impact of each mechanism is crucial as they can influence the IFT of the system and, consequently, the effectiveness of the gas injection method. This study introduces a novel model to assess the influence of different mechanisms on system IFT. The model defines system IFT, adjusts fluid relative permeability to represent miscible, immiscible, and near-miscible states, and utilizes the Buckley–Leverett method to analyze gas fractional flow and saturation profiles when injecting carbon dioxide (CO₂), methane (CH₄), and nitrogen (N₂). Furthermore, the research explores the impact of injection pressure and IFT at minimum miscible pressure (IFT0) on gas injection efficiency. Based on our results, for both live and dead oil, the condensation mechanism reduces IFT and near-miscible pressure; switching to a condensing-vaporizing mechanism increases these parameters. This trend was consistent across all gases studied (N₂, CO₂, CH₄), with a more significant effect observed on the CH₄-live oil system compared to N₂ and CO₂. Controlling the condensing mechanism in IFT measurements enhances gas flow rate and relative permeability curve within the medium. Higher injection pressure in the condensing mechanism and IFT0 = 0.5 leads to faster fluid movement and improved relative permeability due to increased driving forces. Higher IFT0 accelerates the relative permeability of fluids and gas movement within the medium by promoting miscibility sooner. The impact of IFT0 was more pronounced on the dead oil–gas system compared to the live oil–gas system in this study.