Matrix Acidizing of Water Injectors in a Sandstone Field in Saudi Arabia: A Case Study

Al-Anazi, H.A.; Nasr-El-Din, H.A.; Hashem, M.K.; Hopkins, J.A.

doi:10.2523/62825-ms

Cited by 3 publications

(2 citation statements)

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“…This is because of potential precipitation of potassium fluoborate which may damage the formation. 15 Clay minerals in the bulk sample ranged from 0.13 to 6.9 wt%. The clay fraction was separated from the bulk sample and analyzed by XRD.…”

Section: Resultsmentioning

confidence: 99%

Enhancement of Gas Productivity Using Alcoholic Acids: Laboratory and Field Studies

et al. 2006

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe objectives of this study were to: investigate effectiveness of alcoholic acids to stimulate Devonian sandstone gas reservoirs, determine the optimum acid formulation, and recommend the best practices to enhance stimulation treatments in gas wells. In this study, extensive coreflood experiments were conducted on sandstone cores recovered from gas reservoirs to assess the effectiveness of various acid formulations (HCl, HCOOH, HCl/HF, and HCOOH/HF, with and without alcohols) in enhancing gas relative permeability. Several solvents and chemical additives were evaluated to remove or reduce trapped liquids. Experimental results showed commonly used completion fluids caused a severe reduction in gas relative permeability. This study indicated that alcohols can be mixed with stimulation fluid to expedite the cleanup of spent acid and help to attain maximum gas well potential directly after stimulation treatment.Experimental results showed that alcoholic acids have a slower reaction rate with rocks compared to conventional acids having the same acidity. Alcoholic acids can be injected deeper into the formation and their corrosivity can be reduced in hot deep wells with a proper use of corrosion inhibitor type and concentration. Because of low IFT of alcoholic acids, they can be easily injected into tight formation when there are limitations in injection pressure and resulted in deeper stimulation than with conventional acids. This paper discusses a case study on damage diagnosis for a gas well which was recently completed and showed unexpected low productivity. This study proposed an effective stimulation formulation for gas wells and better practices for stimulation design to reduce flowback time needed to lift spent acid and help to achieve maximum production.

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Section: Resultsmentioning

confidence: 99%

Enhancement of Gas Productivity Using Alcoholic Acids: Laboratory and Field Studies

et al. 2006

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“…Unfortunately, environmental concerns and difficulties to achieve the desired fracture length and orientation may discourage hydraulic fracturing [7]. In most cases, matrix acidizing could be challenging to implement in clay rich sandstone formations due to the complex reactions between hydrofluoric acid (HF) and the rock minerals (silicates, feldspars, and chlorites) and the damage that may occur by secondary precipitation particularly at high temperatures [8,9]. In view of the above, we studied an alternative stimulation technology to improve water injectivity in tight and clay rich sandstone reservoir with the application of electrokinetic potential.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Improving Water Injectivity in Tight Sandstone Reservoirs

Alklih

Ghosh

Al-Shalabi

2014

Journal of Petroleum Engineering

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Applicability of electrokinetic effect in improving water injectivity in tight sandstone is studied. DC potential and injection rate are varied for optimization and determination of their individual impact on clay discharge and movement. The liberated clays were characterized through size exclusion microfiltration and ICP-MS analysis. Real time temperature and pH monitoring were also informative. Results showed that severalfold (up to 152%) apparent increase of core permeability could be achieved. Some of the experiments were more efficient in terms of dislodgement of clays and enhanced stimulation which is supported by produced brines analysis with higher concentration of clay element. The results also showed larger quantity of clays in the produced brine in the initial periods of water injection followed by stabilization of differential pressure and electrical current, implying that the stimulation effect stops when the higher voltage gradient and flow rates are no more able to dislodge remaining clays. Additionally, fluid temperature measurement showed an increasing trend with the injection time and direct proportionality with the applied voltage. The basic theory behind this stimulation effect is predicted to be the colloidal movement of pore lining clays that results in widening of pore throats and/or opening new flow paths.

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Machine Learning Application for Wellbore Damage Removal in the Wilmington Field

Kellogg¹,

Chessum²,

Kwong³

2018

SPE Western Regional Meeting

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The use of acid is an important well maintenance tool in removing near wellbore damage to restore a reservoir’s natural permeability and represents one of the most economic options in managing base decline. The selection of acid maintenance candidates however can be a complex process, particularly in wells completed across multiple sands, involving many factors both on the surface and subsurface. As a consequence, individual acid maintenance jobs have had a mixed success rate historically, with certain jobs resulting in a lack of response, or worse, higher water production rates and equipment failures. This paper uses the Wilmington Oil Field, located in southern California, as a case study to examine the typical characteristics associated with low volume acid maintenance success and provides a novel approach using machine learning (ML) algorithms to aid in the screening and selection of future candidates. The developed algorithms, which make use of the open-source statistical software R, is trained based on results from over 500 producer and 3900 injector acid maintenance jobs that were executed at the field and incorporates predictors from the following groupings determined from literature and subject matter experts (SMEs): Production/injection history, Reservoir properties, Acid type and volume, Delivery mechanism, Formation damage, Well completion design, and Surface facility properties. Over 100+ predictor variables were compiled and screened using supervised feature selection to identify those variables providing the greatest explanatory power. A series of machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)) were then used in a classification task to successfully predict whether a producer acid maintenance job would be economic. The logistic regression model ultimately yielded the best classification results (71% prediction accuracy for the producer jobs and 77% for the injector jobs) and proved to be the ML algorithm with the best balance of accuracy, interpretability, and ease of implementation in the field. The model robustness is examined by applying the algorithm outside of the training and test datasets, to acid maintenance jobs executed in 2016-17 and shows similar predictive accuracy. As a result the model is being actively used to automatically screen for treatment candidates among all 700+ producers and 400+ injectors in the Wilmington Field, which are then validated by SMEs before being executed. The overall process has resulted in significant cost savings by both improving the performance of the acid maintenance program and greatly reducing the amount of time spent by technical staff in selecting candidates. These results indicate that ML algorithms can be effective analytical tools not only for ‘big data’ problems (i.e. large-n, time series datasets) which are featured heavily in industry literature, but also for smaller datasets thus opening up a variety of potential applications that can be deployed by surveillance teams alongside traditional approaches.

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Matrix Acidizing of Water Injectors in a Sandstone Field in Saudi Arabia: A Case Study

Cited by 3 publications

References 0 publications

Enhancement of Gas Productivity Using Alcoholic Acids: Laboratory and Field Studies

Enhancement of Gas Productivity Using Alcoholic Acids: Laboratory and Field Studies

A Novel Method for Improving Water Injectivity in Tight Sandstone Reservoirs

Machine Learning Application for Wellbore Damage Removal in the Wilmington Field

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