As the oil industry begins to emerge from one of the worst downturns in decades, companies need to operate in a market of unstable oil prices that the upstream oil and gas industry continues to face. New innovative, sustainable, and disruptive artificial lift technologies are the cost-effective way seek by the operators to minimize risks, maximize production, and remain profitable on the market. In wells with sucker rod-lift systems, elastic beam vibrations induced by downhole pump operation are the main source for premature rod string failure and tubing wear. A completely new steady-state vibrations model for rod string assemblies has been implemented to understand and predict the rod/tubing wear damage phenomenon responsible for half of all well failure events in beam pump operation and most expensive routine well servicing cost. This paper introduces the developed model to enhance prediction of mechanical rod string dynamics during pumping operation, and delivers next generation analytics solution to be utilized for artificial intelligence, industrial internet of things, real-time monitoring, and automation of rod pumping systems. The model works on a set of forced Duffing-like differential equations with cubic non-linearity and damping, these generated discretizing beam elastic behavior using vibration mode basis. Model simulation runs in the time domain numerically capture relevant axial-flexural dynamic forces undergone by rod string during up and down stroke phases, as well as downstroke rod string bending-buckling tendency and its interaction with tubing internal wall. Some examples show usefulness of the novel model to predict failures and maximize uptime, reduce costs with predictive analytics, and design the optimal rod lift solution in every stage of well life cycle.

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Over the last 3 years, Eni has developed an integrated platform to gather subsurface data and make them available to the final users across the company. The Platform is structured in four data domains including the well data, which is the focus of this abstract. In the data model, the well master and architectural data assume paramount importance since they are upstream of the value chain and represent the aggregator of all the data recorded in the well. A large amount of data coming from all Eni Affiliates and operative sites is produced daily. To gather, perform Quality Controls and ingest them, Eni has implemented a governed workflow to ensure data is made available to the final users through the platform in an efficient and transparent way. The workflow aims to ingest the well master and well geometrical data directly from the well site defining roles all along the data transmission chain, with the ultimate objective to ensure the proper data quality once they reach the cross-functional subsurface data platform. It is no less important the timely availability of such data to guarantee a prompt association with geological and drilling log data. Given the increased amount of data acquired from disparate data sources, different functions and locations, Business Intelligence tools have been designed to monitor the workflow combining data for an easier data insight. To manage such complex network, dedicated dashboards allow all the users involved to visualize the status of the processes through specific KPIs, thus optimizing communication and reducing the human effort required. The capability to manage, validate and quickly interpret data will determine the competitive advantage among Energy Companies in the next future. Eni targets to excel in the everchanging business environment leveraging the new century asset: the data. The presented approach, combined with the diffused data culture initiatives, promotes a collaborative environment, and increase awareness on data importance across the value chain.

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Berkine basin is one of the main oil producers in Algeria. The upper, middle, and lower TAG-I are the target oil-bearing sands. In this basin, the ROD field is under pressure maintained mainly through water injection together with, to a lesser extent, gas injectors. The southern part of the field, "ROD Tail" has four water injectors targeting the middle TAG-I. In recent evaluation conducted through pressure measurement and an interference test, reservoir pressure was found to have declined by 35 bar within 2 years. This has prompted questions about reservoir management, mainly about the effectiveness of injector well capacity in maintaining reservoir pressure. Extensive data were gathered through well intervention; cleanout, perforation, and a caliper log. Many failed acid jobs were also noted in the history of these wells. An engineered high-pressure jetting operation via coiled tubing was executed, but injectivity could not be restored. A methodology and workflow were adopted to identify the source of formation damage and scale deposition in the near-well area and around perforations. Solid samples were collected from the well and sent to laboratory to characterize formation damage type. The injection water was also analyzed by performing a standard 12-ion concentration analysis. An aqueous model simulator was used to confirm that the water was supersaturated with CaSO4 and CaSO4.2H2O. Finally, clay acid treatment was found to be effective. The treatment fluid was designed to prevent proppant dissolution and to clean fracture matrix interface. This was the first time this type of operation was executed after many unsuccessful conventional acidizing operations. Excellent results were obtained after the acid stimulation treatment. The injection rate was found to increase significantly from 120 m3/d to 360 m3/d. Water injection pressure was also found to decrease from 243 bar to 220 bar, and the injectivity index increased by three times. Near-wellbore formation damage was removed, and formation permeability recovered. The clay acid treatment was applied to other wells in the field and similar results were obtained.