Summary Determination of relative permeability data is required for almost all calculations of fluid flow in petroleum reservoirs. Water/oil relative permeability data play important roles in characterizing the simultaneous two-phase flow in porous rocks and predicting the performance of immiscible displacement processes in oil reservoirs. They are used, among other applications, for determining fluid distributions and residual saturations, predicting future reservoir performance, and estimating ultimate recovery. Undoubtedly, these data are considered probably the most valuable information required in reservoir simulation studies. Estimates of relative permeability are generally obtained from laboratory experiments with reservoir core samples. In the absence of the laboratory measurement of relative permeability data, developing empirical correlations for obtaining accurate estimates of relative permeability data showed limited success, and proved difficult, especially for carbonate reservoir rocks. Artificial-neural-network (ANN) technology has proved successful and useful in solving complex structured and nonlinear problems. This paper presents a new modeling technology to predict accurately water/oil relative permeability using ANN. The ANN models of relative permeability were developed using experimental data from waterflood-core-tests samples collected from carbonate reservoirs of giant Saudi Arabian oil fields. Three groups of data sets were used for training, verification, and testing the ANN models. Analysis of results of the testing data set show excellent agreement with the experimental data of relative permeability. In addition, error analyses show that the ANN models developed in this study outperform all published correlations. The benefits of this work include meeting the increased demand for conducting special core analysis (SCAL), optimizing the number of laboratory measurements, integrating into reservoir simulation and reservoir management studies, and providing significant cost savings on extensive lab work and substantial required time.

The rapidly evolving tablet and mobile devices have proven their overwhelming popularity, not just as entertainment devices but as valuable productivity tools for enterprises and professionals. Furthermore, mobile computing is becoming an integral player in the Information Technology industry, thereby influencing the design of software applications required by oil and gas organizations. The new generation of tablets and smartphones can be effectively utilized for real time data monitoring and reporting. This can be achieved through the deployment of Upstream software solutions, such as GIS supported reservoir performance monitoring applications, management reporting dashboards, and engineering analytical tools. This paper provides an overview of an assessment project initiated by Saudi Aramco to evaluate the development and deployment of oil and gas Upstream software solutions on mobile devices. The assessment focused on capitalizing on the capabilities of these devices for adding business value and building in-house expertise in the area of mobile application development. The paper will summarize the assessment findings and recommendations for the future direction of applications development methodology and requirements.

Well placement and trajectory planning is a critical and challenging task in any Oil & gas field development plan. This has always been a task that requires a strong team effort to ensure the best predictions and estimations are performed to penetrate the desired reservoir development zones and achieve the expected production rates. Numerous iterations with a high level of interaction between Reservoir Management, Reservoir Simulation, Reservoir characterization, Geology and Geophysics are expected when planning a new well or a difficult sidetrack. Each member of the well planning team has his/her own environment along with dedicated standalone software tools. Today, with the introduction of the Intelligent Field center to provide the collaboration environment for the involved expertise along with the proper tool to offer a strong integration of different data sources in a single software environment that allows a team of field geologist, geophysicist, reservoir and simulation engineers to evaluate the best prospects available in planning any new drilling opportunity, furthermore the team is able to choose the best well azimuth and inclination based on reservoir properties and performance. This paper discusses a newly integrated well planning process utilizing a collaboration environment to review seismic, well log cross sections, historical production and simulation predictions enabling the well planning team to place their gas wells using a reservoir 3D viewer, incorporating the reservoir simulation model, and run various cases and sensitivities. In This paper, full examples illustrate how effective software integration can optimize the time required to plan a new well without overlooking any important aspect. In addition, the paper discusses the lessons learned from this experience and how this approach changes the mechanism of an integrated well planning team working together.

The rig operations and drilling activities around the Kingdom have drastically increased over the past few years. Tracking such operations tends to be an extremely complicated process. Furthermore, providing executive management with manual status update reports of the different jobs proves to be a time consuming and laborious task. Such executive summary reports involve collecting information from different sources, which becomes a full time daily job for the engineers, taking into account the size of the operation. With all these challenges, a solution was proposed to develop a streamlined approach to automate such a process. This solution was designed and implemented to be a one stop shop for all logging and well testing operation activities. The solution integrates data from different sources and provides the engineers with all the required information to kick-start the analysis. This Management Operations Dashboard (MOD) enriches the decision making process by providing Upstream management with a high level, multilateral status overview of the operations, in a timely manner through a GIS enabled interface. It also automates the process of generating and distributing executive summary reports to higher management, allowing engineers to focus on analysis and setting strategies.The paper discusses and shares major insights from the completed project and highlights the main incorporated features. It also outlines the MOD's workflow to prepare and send summarized operation reports.

Well placement and trajectory planning is a critical and challenging task in any Oil & gas field development plan. This has always been a task that requires a strong team effort to ensure the best predictions and estimations are performed to penetrate the desired reservoir development zones and achieve the expected production rates. Numerous iterations with a high level of interaction between Reservoir Management, Reservoir Simulation, Reservoir characterization, Geology and Geophysics are expected when planning a new well or a difficult sidetrack.Each member of the well planning team has his/her own environment along with dedicated standalone software tools. Today, with the introduction of the Intelligent Field center to provide the collaboration environment for the involved expertise along with the proper tool to offer a strong integration of different data sources in a single software environment that allows a team of field geologist, geophysicist, reservoir and simulation engineers to evaluate the best prospects available in planning any new drilling opportunity, furthermore the team is able to choose the best well azimuth and inclination based on reservoir properties and performance.This paper discusses a newly integrated well planning process utilizing a collaboration environment to review seismic, well log cross sections, historical production and simulation predictions enabling the well planning team to place their gas wells using a reservoir 3D viewer, incorporating the reservoir simulation model, and run various cases and sensitivities. In This paper, full examples illustrate how effective software integration can optimize the time required to plan a new well without overlooking any important aspect. In addition, the paper discusses the lessons learned from this experience and how this approach changes the mechanism of an integrated well planning team working together.