An important aspect for the exploitation of gas condensate reservoirs is the knowledge of the maximum retrograde condensation, due to the volume of liquid that can be trapped if the pressure reaches the zone where this phenomenon occurs because hardly reaches critical fluid saturation as so it can move toward wells. This work proposes a correlation that can be used to predict or validate this property, based on the relationship between the maximum retrograde condensation, the molecular weight (MW) of the original composition and its gas oil ratio. A real case analysis is presented which corrects the maximum retrograde condensation measured in the laboratory, reproducing the full curve of this property, without changing the thermodynamic conditions of the fluid. Also, a conceptual numerical simulation model was built to quantify the behavior and effects if is not corrected the retrograde condensation property in a PVT study.
Naturally fractured reservoirs (NFR) are highly complex from their characterization to thier exploitation; their behavior depends on two systems: the fracture and the matrix. This complex nature hinders the development and adjustment of numerical models; most parameters present high uncertainties. Simulation engineers spend a lot of time obtaining a representative and reliable model. This work was developed with the purpose to establish an assisted history matching methodology using an evolution strategy algorithm (ESA) to accelerate the construction of this reliable model. Evolution algorithms were first showcased in the 1960's and in recent years have expanded their use as a mechanism for acceleration of history matching or production optimization.Additionally we used ESA to relocate already programmed wells and thereby new potential areas for exploitation were identified, where wells weren't considered in the initial development strategy, managing to increase the ultimate recovery factor (URF). Evolution Strategy Algorithm was used successfully in a field of the south of Mexico with a numerical model available, which has a 4 year production history through 14 active wells. The field is a NFR with high production potential and strong water breakthrough in the wells. Starting from a previous simulation model, we establish 3 stages for the proposed work flow:Analysis of geological variables, the discrete fracture network (DFN) and identifying and weighting the impact on the dynamics of both fluids systems. Application of ESA using the variables identified on stage 1, analyzing several simulation models and using an objective function to quantify the miss match from our ideal model. Optimization of the proposed wells, evaluation of areas with exploitation potential and proposing of new wells in these areas using ESA.Using ESA we manage to optimize simulation times and to achieve a reliable and representative model in 75% less time than with our previous effort. This history matched model includes a DFN and reproduces the water breakthrough behavior on 90% of the wells and in 100% of wells with higher oil and water production. The result model was used to relocate the next two proposed wells into a new area applying ESA; where the cumulative oil production was maximized at the end of the simulation period, increasing URF in 2.5% respect to the original development plan and the interference with neighborhood wells were minimized.Finally as consequence of proposed well relocation, we found a new area in the field with potential to allocate an additional well. Setting the ESA to maximize cumulative oil production yield to the location optimization on this new well, thus increased URF another 1.2%. Assisted history matching using ESA reduces substantially time analysis compared with traditional methods and, it was possible to have a reliable model with DFN that reproduces water breakthrough and captures the heterogeneity of the fractured system. The methodology proposed helps to accelerate decision-making with more tec...
In the literature, there isn't a unified criterion to establish a methodology for quality assessment of PVT analysis. Worldwide, engineers have proposed the use of well-known validation methodologies but these validation methodologies not necessarily encompasses the whole consistency of a PVT study or its representativeness of the reservoir fluids. It is well known that a PVT study might have a great consistency after it's validated but when it's used in a reservoir characterization it didn't reproduce the measured behavior of the fluid present in the reservoir. These types of inconsistencies are common and could cause very important errors in analysis such as: Original volume, reserves estimation, surface facilities planning and exploitation strategies. The impact on the results and decisions taken based on the information present in a PVT study can be crucial and therefore every PVT analysis should include a level of uncertainty index that could allow to know beforehand the cost of using such information. The versatility and complexity of most the experiments conducted during a PVT study makes the task of creating a unique methodology of quality quantification difficult. The present paper proposes a methodology for quality control and uncertainty assessment. This methodology considers each of the stages involved in the process of obtaining a PVT analysis: from the fluid sampling to the experiments results. According to previous work and field experience, the most important parameters or conditions were selected at each stage. These parameters are called Critical Points; each Critical Point has and associated impact level (high, medium and low.) and weight that reflects directly in the overall quality index of the PVT study. Critical points were subsequently grouped into 6 Control Categories: Sampling conditions, reservoir conditions, experiments control, measured properties control, fluid type validation and validation of experiment consistency. An ideal value of 10.0 was established for a report PVT that has excellent condition, very low uncertainty and meets all the conditions proposed in this paper. Additionally, a Quality Index (QI) and Validation Index (VI) were proposed as two indicative values of the level of uncertainty of any PVT study. Using a proposed Nomograph, engineers can evaluate and categorize the quality results from any PVT study. The results from applying this methodology to 50 PVT studies are presented and a Material Balance and numerical simulation study were carried out to exemplify the impact of using for the same reservoir, PVT from different level of quality. The goal of this work is to have a practical methodology that can give engineers a "Quality Index" of any PVT study. This quality index could be an indicative of the level of uncertainty and the degree of reliability of the PVT study before it's subsequently used in any reservoir or well analysis. This methodology could be used worldwide without limitations
Naturally fractured reservoirs (NFR) are generally subdivided into segments that behave as separate flow units due to the flow barriers (areal and vertical) generated by multiple fractures networks present in the reservoir. This implies a challenge to predict the dynamic behavior of a reservoir when the degree of compartmentalization and blocks that form the reservoir is unknown. Generally, reservoir compartments are identified by geological events and not based on the dynamics. This work proposes a methodology that attempts to describe and identify the compartments of a NFR through a dynamic analysis including: – Production behavior (water, water-salinity, oil and GOR) – THP and BHP behavior (HPHT real time data) – Orientation and intensity of fractures – Analysis of the compositional variation of the fluid (vertical and areal) – Multi-tank model analysis (Material Balance) to identify faults transmissibility using in the simulation model The proposed methodology was successfully applied to a NFR in the South of Mexico that presents problems in the History Matching process in its numerical simulation model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.