Restoring Well Productivity Through a Fit-for-Purpose Sludge Cleanout Job

2021 ◽  
Author(s):  
Muhammad A Al Huraifi ◽  
Ali A Al-Taq ◽  
Muhammad A Hajri
Keyword(s):  
Thick Layer ◽  
Low Ph ◽  
Surface Flow ◽  
Gas Oil ◽  
Well Performance ◽  
Sample Collection ◽  
Separation Plant ◽  
Sludge Formation ◽  
Oil Separation ◽  
Well Productivity

Abstract Sludge formation could significantly impair well productivity if deposited in the wellbore or surface flow lines. In a field where sludge formation is not common, an oil production well showed a sudden deterioration in well productivity. Thorough investigation of abnormal well performance, from surface and sub-surface perspective, indicated that the deposition of a thick layer of a tight emulsion across the surface choke has resulted in ceasing the oil flow to the gas oil separation plant. Extensive lab analysis indicated that the obstruction material was a sludge deposition promoted by the presence of asphaltene, high amount of iron and low pH brine. It is noteworthy to mention that the analytical results of lab prepared emulsion samples elucidate the rule of low pH aqueous solution, asphaltene and iron ions in inducing tight emulsion formation which helps to understand the root causes of sludge deposition. To come up with a cost-effective remedial treatment considering health, safety and environment (HSE), different emulsion breaking formulations, including different de-emulsifiers and anti-sludge agents, were examined in this study. An effective diesel-based formulation including proper de-emulsifier and anti-sludging agent was used during the execution of the field job. The design of the field job took into consideration a minimal footprint to the environment through the flowback of the well to the neighboring gas oil separation plant. This paper summarizes the joint efforts by production engineers and lab scientists to systemically tackle such major flow assurance issues which could significantly jeopardize wells productivity. The systemic approach starts with problem detection through well intervention and sample collection. It also includes the lab work which was carried out to identify the type and composition of deposition and evaluate/optimize a proper formulation for sludge deposition removal. The paper discusses in detail the design and execution of a successful field treatment, which has resulted in restoring and maintaining the well potential.

Troubleshooting Gas Dehydration Systems Using Data Analysis

2021 ◽  
Author(s):  
Abdullah Al-Aiderous
Keyword(s):  
Data Analysis ◽  
Operating Temperature ◽  
Triethylene Glycol ◽  
Gas Oil ◽  
Separation Plant ◽  
Oil Separation ◽  
Analysis Techniques ◽  
Using Data ◽  
Product Specifications

Abstract The objective of this paper is to showcase the successful and innovative troubleshooting data analysis techniques to operate a TEG dehydration system optimally and reduce glycol loss and to meet the product specifications in one of the gas dehydration systems in an upstream gas oil separation plant (GOSP). The gas dehydration system using Triethylene Glycol (TEG) is the most widely used and reliable gas dehydration system in upstream operation. These proven data analysis techniques were used to tackle major and chronic issues associated with gas dehydration system operation that lead to excessive glycol losses, glycol degradation, and off-specification products. Glycol loss is the most important operating problem in the gas dehydration system and it represents a concern to the operation personnel. Most dehydration units are designed for a loss of less than 1 pound of glycol per million standard cubic feet of natural gas treated, depending on the TEG contactor operating temperature. In this paper, comprehensive data analysis of the potential root causes that aggravate undesired glycol losses degradation and off-specification products will be discussed along with solutions to minimize the expected impact. For example, operating the absorption vessel (contactor) or still column at high temperature will increase the glycol loss by vaporization. Also, the glycol losses occurring in the glycol regenerator section are usually caused by excessive reboiler temperature, which causes vaporization or thermal decomposition of glycol (TEG). In addition, excessive top temperature in the still column allows vaporized glycol to escape from the still column with the water vapor. Excessive contactor operating temperature could be the result of malfunction glycol cooler or high TEG flow rate. This paper will focus on a detailed case study in one of the running TEG systems at a gas-oil separation plant.

Risk-Based Assessment (RBA) of a Gas/Oil Separation Plant

2002 ◽  
Author(s):  
Norman Smith ◽  
Omar I. BuTuwaibeh ◽  
Ivan C. Cruz ◽  
Moraya S. Gahtani
Keyword(s):  
Gas Oil ◽  
Separation Plant ◽  
Oil Separation

Innovative Integrated and Compact Gas Oil Separation Plant for Upstream Surface Facilities

2020 ◽  
Author(s):  
Mohamed Ahmed Soliman ◽  
Samusideen Salu ◽  
Talal Al-Zahrani ◽  
Nisar Ansari
Keyword(s):  
Gas Oil ◽  
Separation Plant ◽  
Oil Separation

scholarly journals An Improved Method to Estimate Soil Hydrodynamic and Hydraulic Roughness Parameters by Using Easily Measurable Data During Flood Irrigation Experiments and Inverse Modelling

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1581
Author(s):  
Mohamed Alkassem Alosman ◽  
Stéphane Ruy ◽  
Samuel Buis ◽  
Patrice Lecharpentier ◽  
Jean Bader ◽  
...  
Keyword(s):  
Soil Surface ◽  
Surface Flow ◽  
Performance Criteria ◽  
Soil Parameters ◽  
Well Performance ◽  
Estimation Errors ◽  
Hydraulic Roughness ◽  
Infiltration Model ◽  
Estimation Uncertainty ◽  
Using Data

Surface irrigation is known as a highly water-consuming system and needs to be optimized to save water. Models can be used for this purpose but require soil parameters at the field scale. This paper aims to estimate effective soil parameters by combining: (i) a surface flow-infiltration model, namely CALHY; (ii) an automatic fitting algorithm based on the SIMPLEX method; and (iii) easily accessible and measurable data, some of which had never been used in such a process, thus minimizing parameter estimation errors. The validation of the proposed approach was performed through three successive steps: (1) examine the physical meaning of the fitted parameters; (2) verify the accuracy of the proposed approach using data that had not been served in the fitting process; and (3) validate using data obtained from independent irrigation events. Three parameters were estimated with a low uncertainty: the saturated hydraulic conductivity Ks, the hydraulic roughness k, and the soil water depletion ∆θ. The estimation uncertainty of the soil surface depressional storage parameter H0 was of the same order of magnitude of its value. All experimental datasets were simulated very well. Performance criteria were similar during both the fitting and validation stages.

scholarly journals Application of Artificial Intelligence Techniques to Predict the Well Productivity of Fishbone Wells

2019 ◽  
Vol 11 (21) ◽  
pp. 6083 ◽  
Author(s):  
Amjed Hassan ◽  
Salaheldin Elkatatny ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Absolute Error ◽  
Well Performance ◽  
Well Productivity ◽  
Artificial Intelligence Models ◽  
Actual Flow ◽  
Artificial Neural ◽  
Effective Models

Fishbone multilateral wells are applied to enhance well productivity by increasing the contact area between the bottomhole and reservoir region. Fishbone wells are characterized by reduced operational time and a competitive cost in comparison to hydraulic fracturing operations. However, limited models are reported to determine the productivity of fishbone wells. In this paper, several artificial intelligence methods were applied to estimate the performance of fishbone wells producing from a heterogeneous and anisotropic gas reservoir. The well productivity was determined using an artificial neural network, a fuzzy logic system and a radial basis network. The models were developed and validated utilizing 250 data sets, with the inputs being the permeability ratio (Kh/Kv), flowing bottomhole pressure and lateral length. The results showed that the artificial intelligence models were able to predict the fishbone well productivity with an acceptable absolute error of 7.23%. Moreover, a mathematical equation was extracted from the artificial neural network, which is able to provide a simple and direct estimation of fishbone well productivity. Actual flow tests were used to evaluate the reliability of the developed model, and a very acceptable match was obtained between the predicted and actual flow rates, wherein an absolute error of 6.92% was achieved. This paper presents effective models for determining the well performance of complex multilateral wells producing from heterogeneous reservoirs. The developed models will help to reduce the uncertainty associated with numerical methods, and the extracted equation can be inserted into commercial software, thereby significantly reducing deviation between the actual data and simulated results.

EVALUATION AND MANAGEMENT OF THIN OIL COLUMN RESERVOIRS IN AUSTRALIA

1994 ◽  
Vol 34 (1) ◽  
pp. 64 ◽  
Author(s):  
H. R. Irrgang
Keyword(s):  
Column Height ◽  
Gas Oil ◽  
Well Performance ◽  
Oil Viscosity ◽  
Heterogeneous Reservoirs ◽  
Vertical Permeability ◽  
Well Spacing ◽  
Hydrocarbon Reserve ◽  
Water Cut ◽  
Dipping Strata

Thin oil columns represent a common and important class of hydrocarbon reserve which are notoriously difficult to evaluate and produce. This paper provides case studies of examples of these reservoirs in Australia and summarises the production methods, well performance and recovery efficiencies.Thin oil column reservoirs are defined here as reservoirs which will cone both water and gas when produced at commercial rates. The oil zone can have a pancake or rim geometry. Examples within Australia include Bream and Snapper (Gippsland Basin), South Pepper and Chervil (Carnarvon Basin), Chookoo (Eromanga Basin) and Taylor (Surat Basin).Parameters which are particularly important in defining the performance of these reservoirs are: horizontal and vertical permeability, column height, stratigraphie dip, well spacing, and oil viscosity. High horizontal permeability is more critical than in other reservoir types as it controls the effectiveness of gravitational forces in opposing coning and other unwanted flows by reducing pressure gradients. Low vertical permeability mitigates coning but can limit across strike drainage in dipping strata. Oil viscosity is also particularly important, even when the mobility ratio is favourable, as it controls the gas/oil ratio and water cut during coning.As coning (by definition) is inevitable the key production issue is gas cap management. The main options are:Limit gas coning by controlling completion depth and production rates.Allow gas cap shrinkage and 'chase' the oil column upwards via recompletions.Reinject gas to control gas-oil contact position.For the latter two options in particular, ultimate reserves are a strong function of the capacity of the installed production facilities, especially offshore, where fixed operating costs are high. When gas cap management is not compromised, reserves increase with higher total fluid withdrawal rates. Examples of the various gas cap management and production strategies are included.Both horizontal (South Pepper, Bream) and conventional (Chookoo, Taylor) completion techniques have been applied to thin oil column reservoirs in Australia. Horizontal completions can increase productivity, mitigate coning and increase the well drainage areas (particularly if drilled across dip in heterogeneous reservoirs). However, horizontal completions are particularly vulnerable to poor cement jobs, natural fractures and undesirable fluid contact movements.A variety of other completion techniques have been tried worldwide in thin oil columns with mixed success. These include multiple completions in the water, oil and/or gas to allow separate production, and injection of fluids to make permeability barriers or alter relative permeability.A number of scaling rules are included to assist in using offset field data for evaluation of thin oil column reservoirs. Improved understanding of these complex reservoirs will maximise their economic potential.

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