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Keywords: prediction
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Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216738-MS
... In the present work, we introduce a multilinear regression-based tool to predict key quantities for engineering disciplines by using As-built Bill of Quantities (BOQ) / Material Take-off (MTO) data from previously executed EPC projects by the company. The developed tool leverages the knowledge...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216769-MS
... inductive learning asia government artificial intelligence wellbore seismic deep learning production monitoring reservoir characterization poor quality image prediction misleading interpretation scenario reservoir geomechanics geological subdiscipline feature overlapping feature application...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216779-MS
... Abstract Efficient drilling operations in the oil and gas field is an important area that can lead to major cost and hazard reduction. One of the key parameters for drilling optimization is predicting the rate of penetration. The penetration rate depends on the physical process which contains...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216916-MS
... well, an axisymmetric stress distribution, and either plane strain during gas production, or plane stress during injection. This simplified solution is compared with the predictions of a commercial 3D finite-element (FE) code applied to the near well regions. The application concerns a well...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216936-MS
... digital fields to track the well performance and minimize the error in back allocated rates as it was observed in this field application, demonstrating how reconciliation factor was continuously enhanced day after day, once AI-virtual rate prediction was introduced. AI-model prediction was later verified...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216935-MS
... representations. Subsequently, a neural network model is trained to process the compressed representations along with completions and production parameters, enabling simultaneous prediction of monthly condensate and gas production rates over a 5-year decline period for hydraulically fractured shale wells...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-217004-MS
..., it is of practical interest for engineers to develop a fast, reliable tool that extracts knowledge from historical data and use it to analyze and predict well performance and support future process design decisions. Artificial intelligence (AI) is becoming increasingly important in the industry for providing...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216783-MS
... compositions with focus on the plus fraction amount in fluids with a GOR greater than 600 Sm 3 /Sm 3 . A new modeling workflow handling uncertainty in the plus fraction amount to improve the accuracy of predicted EoS simulations is presented. The paper presents a comprehensive review of previous studies...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216980-MS
... critical rotating equipment availability is also growing. It became imperative to predict machine failures in advance to take proactive mitigative actions. ADNOC Onshore collaborated with parent company ADNOC Upstream to deploy machine learning based CPAD solution coupled with thermodynamics first...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216998-MS
... can pose significant computational challenges. The inherent reactivity of various minerals complicates the modeling process, leading to computationally expensive simulations. Therefore, the objective of this study is to develop a deep-learning workflow that can predict the changes in CO 2...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216990-MS
... of production rates and the assessment of equipment health for failure prediction. Historical data logs from two Middle East fields, operating about 100 instrumented wells, are used to validate the work and develop a failure detection process using machine learning (ML). The production model utilizes Nodal...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216731-MS
... Abstract The purpose of this work is to create a stable and scalable digital twin of oil well for virtual flow metering task capable of predicting the flow rate of wells equipped with ESPs for many fields in Western Siberia at various stages and conditions of development. The basis...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216592-MS
..., requiring expensive and time-consuming rheological lab experiments. Machine learning has recently gained popularity and has been applied successfully in many sectors, including the oil & gas industry, to develop predictive models and forecasting tools. With more than 30 years of experience in polymer...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216247-MS
... that is typically used to evaluate patterns, a novel approach is presented to combine connectivity (obtained from production/injection data history) and remaining oil saturations. This novel workflow evaluates polymer injection patterns and predicts their impact on oil production. This study proposes a hybrid model...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216218-MS
... Abstract At surface facilities, a gas field with condensate and a severe oil in water/reverse emulsion problem was observed. Due to the emulsify-prone nature of liquid production, this field suffers from Oil in Water content. Current modeling prediction tool cannot be used to predict...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216238-MS
... such as field-control parameters (oil production rate cutoff, water injection rate cutoff, etc.) as well as well-related parameters, among others, referred to as FDP constraints. Our approach uses a pipeline of deep learning models to accurately predict these parameters, which in turn generate the economic...
Proceedings Papers

Paper presented at the ADIPEC, October 2–5, 2023
Paper Number: SPE-216231-MS
... government reservoir characterization identification application clastic rock fracture neural network artificial intelligence machine learning de groot 2006 society ant-tracking structural geology ant tracking output prediction fracture detection africa government rock type tunisia...

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