Mansoor Zoveidavianpoor

Development of a Fuzzy System Model for Candidate-well Selection for Hydraulic Fracturing in a Carbonate Reservoir

With current technology, it is only possible to extract 20% to 25% of the original oil in place from Iranian carbonate reservoirs, 10% less than the world average. In addition, formation damage is a serious problem in those reservoirs, which mainly caused by asphaltene precipitation, sand production, and ineffective stimulation method. The majority of mature carbonate reservoirs in Iran have low permeability and high skin values. Therefore, such reservoirs are capable of producing at commercial rates only if they are hydraulically fractured. Acid fracturing is usually reported as a standard method for fracturing in carbonate reservoirs. Hydraulic Fracturing (HF) technology, which was originally applied to overcome near wellbore damage, is a proper replacement stimulation method. It is evident that to adopt this technology, considerable efforts have to be strenuous in candidate-well selection. As asserted in the literature, even though a common practice, candidate-well selection is not a straightforward process and up to now, there has not been a well-defined approach to address this process. The techniques applied in HF candidate-well selection could be divided into two methods; conventional and advanced approaches. Conventional methods are not easy to use for nonlinear processes, such as candidate-well selection that goes through a group of parameters having different attributes and features such as geological aspect, reservoir and fluid characteristics, production details, etc. and thats because it is difficult to describe properly all their nonlinearities. However, it is believed that advanced methods such as Fuzzy Logic (FL) could be better decrease the uncertainty existed in candidate-well selection. This paper presents a Mamdani fuzzy model where rules for HF candidate-well selection were derived from multiple knowledge sources such as existing literature, intuition of expert opinion to verify the gathered information. The needs for adapting HF as replacement stimulation in Iranina carbonate reservoirs are discussed and advanced methods for HF candidate selection will be reviewed in this paper. Also, the main reasons which show why propped HF is the choice in carbonate reservoirs will be discussed. Finally, the proposed Fuzzy system model is applied along with a case study in a carbonate reservoir.

The Incorporation of Silica Nanoparticle and Alpha Olefin Sulphonate in Aqueous CO2 Foam: Investigation of Foaming Behavior and Synergistic Effect

Foam behavior and synergistic effect of aqueous CO2 by three different sizes of hydrophobic silica nanoparticles with varying concentrations in presence of single anionic surfactant alpha olefin sulphonate with constant concentration of 1000 ppm were investigated in detail. The role of each size of silica nanoparticle was analyzed in view of the foamability, foam stability, and synergistic effect after equilibrium of aqueous CO2 foam. Result reveals that the bigger size of silica nanoparticle has better effect in low concentration and generally smaller size of silica nanoparticle has better influence on CO2 foam stability at higher concentrations.

Prediction of compressional wave velocity by an artificial neural network using some conventional well logs in a carbonate reservoir

As vital records for the upstream petroleum industry, compressional-wave (Vp) data provide important information for reservoir exploration and development activities. Due to the different nature and behaviour of the influencing parameters, more complex nonlinearity exists for Vp modelling purposes. Therefore, formulating a prediction tool that can accurately estimate the lacking log data, such as Vp, is of prime importance. Therefore, an attempt has been made to develop a prediction model for Vp as a function of some conventional well logs by using an artificial neural network (ANN). The obtained results are compared to those of multiple linear regression (MLR) models. A total of 2156 data points from a giant Middle Eastern carbonate reservoir, derived from a conventional wire line and a dipole sonic imager log were used in this study. The efficiency of the employed approach, quantified in terms of the mean squared error correlation coefficient (R-square), and prediction efficiency error, is evaluated through simulation and the results are presented. The result showed that an ANN outperforms MLRs and was found to be more robust and reliable.

A comparative study of artificial neural network and adaptive neurofuzzy inference system for prediction of compressional wave velocity

Abstract In this study, two solutions for prediction of compressional wave velocity (p wave) are presented and compared: artificial neural network (ANN) and adaptive neurofuzzy inference system (ANFIS). Series of analyses were performed to determine the optimum architecture of utilized methods using the trial and error process. Several ANNs and ANFISs are constructed, trained and validated to predict p wave in the investigated carbonate reservoir. A comparative study on prediction of p wave by ANN and ANFIS is addressed, and the quality of the target prediction was quantified in terms of the mean-squared errors (MSEs), correlation coefficient (R2) and prediction efficiency error. ANFIS with MSE of 0.0552 and R2 of 0.9647, and ANN with MSE of 0.042 and R2 of 0.976, showed better performance in comparison with MLR methods. ANN and ANFIS systems have performed comparably well and accurate for prediction of p wave.

Does the Maturity of Jatropha Curcas L. Affect the Quality and Quantity of the Yield of Oil for Biodiesel Production

Biodiesel is a green and popular renewable fuel and unlike mineral diesel, produces fewer toxic emissions. Jatropha curcas Linn is a nonedible fruit that commonly used in biodiesel production. This study evaluated the impacts of Jatropha seeds maturity on quantity and quality of yield oil. Production of biodiesel from Jatropha oil and ethanol using natrium hydroxide as the catalyst by transesterification was performed on half-matured (yellow) and matured (black) Jatropha seeds. Experimental investigations have been carried out to examine properties and performance of Jatropha, and different blends of Jatropha oil-diesel (JOD) in comparison to a petroleum-based diesel fuel. Half-matured stage of Jatropha is shown to produces 2.5% less oil than the matured stage. The quality of the maturity stages quantified in terms of mean square error (MSE), and the matured stage showed 13% better performance in contrast to the half-matured stage. Matured stage JOD blends indicate closer performance to petroleum-based diesel and can be used as a biodiesel without engine modification.

Applications of type-2 fuzzy logic system: handling the uncertainty associated with candidate-well selection for hydraulic fracturing

Abstract The problem of selecting a target formation(s) in a reservoir among a vast number of zones/sub-layers within huge number of hydrocarbon producing wells for hydraulic fracturing (HF) by using interval type-2 fuzzy logic system (IT2-FLS) to maximize their net present value is studied in this paper. Classical fuzzy system which is called type-1 fuzzy logic system is not capable of accurately capturing the linguistic and numerical uncertainties in the terms used and the inconsistency of the expert’s decision-making. IT2-FLS is very useful in circumstances where it is difficult to determine an exact membership function for a fuzzy set; hence it is very effective for dealing with uncertainties. In highlighting this need, the question has been answered why IT2-FLS should be used in this study. The procedure of applying this study in the area of HF candidate-well selection is illustrated through a case study in an oil reservoir.

The Foaming Behavior and Synergistic Effect in Aqueous CO2 Foam by In Situ Physisorption of Alpha Olefin Sulfonate and Triton X-100 Surfactants and Their Mixture

Foaming behavior is an important consideration in the design of many foam-based operations and processes. The foam behavior and synergistic effect of aqueous CO2 by the single anionic surfactant alpha olefin sulfonate (AOS), nonionic single surfactant Triton X-100 (TX100), and mixed surfactant solution (AOS-TX100) with varying concentrations were investigated in detail. The role of each surfactant and mixed surfactant solutions were analyzed in view of the foamability, foam stability, and synergistic effect after equilibrium of aqueous CO2 foam. It was found that AOS and the composite surfactant AOS-TX100 (8:2) had better foaming behavior than any single surfactant and mixed surfactants.

Well Stimulation in Carbonate Reservoirs: The Needs and Superiority of Hydraulic Fracturing

Hydraulic fracturing is probably the most widely used stimulation technique in the world today. The focus of this article is on choosing a proper method of hydraulic fracturing. In carbonates, a choice exists between acid and propped fracturing treatments. The main reasons showing why propped hydraulic fracturing is the choice in carbonate reservoirs will be discussed in detail in this article.

Health, safety, and environmental challenges of xylene in upstream petroleum industry

Xylene sited in the Volatile Organic Compounds (VOCs), is considered as one of the toxic chemicals released to environment during oil and gas operations in order to remove the organic deposits such as asphaltene. Normally, wellbore soaking by xylene is performed to remove the organic plugs in the petroleum production system. However, xylene imposed detrimental impacts and continuous threat to field personnel and environment via storage and its flow backs into the waste pit. This paper illustrates oil well stimulation process and equipment and reviews the environmental challenges of xylene and gives indication of credible alternatives in stimulation oil and gas wells. This way, awareness is provided for upstream petroleum industry and environmental engineers for its risk evaluation, development of remediation and/or replacement techniques, and finally identification of future research directions.

A Local Computerized Multi-Screening of Vast Amount of Data to Select Hydraulic Fracturing Candidates in Iranian Carbonate Oil Fields

Iranian oil companies are developing the technique of Hydraulic Fracturing (HF) operation to enhance the hydrocarbon recovery of deep carbonate formations. However, there is not a computerized tool or well defined framework for Iranian carbonate oil fields to select candidates. The ineffective HF experiences in the past emphasized that candidate selection is the frontline of a victorious HF operation. This paper presents the development of a local programme to automatically select specific zones for special purposes like HF. The program is written in MATLAB in such a way to integrate large amount of data from different disciplines. In addition, the missing data are compensated with Neural Network and Fuzzy Logic techniques. In the end data are mechanically screened based on the user selected parameters, cut-offs and weight factors. Results of screening within the limitations are prioritized in stacked bars to make decision easier. This tool is applied for a purpose of candidate selection for HF in M oil field located in south of Iran. This field has 585 zones which each zone has more than 30 parameters form different disciplines. The result of this programming is printed schematically and it is conclusive to our clients.