No abstract

As conventional hydrocarbon resources in current basins of the world continue to decline, heavy oil becomes a significant candidate to fulfill the world's thirst for energy. In China, the potential reserves of heavy oil amount to 220 billion BOE, with more than 13% in offshore fields. For Bohai Bay, up to 80% of developed reservoirs are heavy oil and unconsolidated sandstone reservoirs.Due to the geological process and depositional environment in generating heavy oil, the common characteristics of such oils are high viscosity, low gravity, high asphaltenes, and weak homogeneity and cementation. As a result, the productivity of heavy oil is usually low in a single well, accompanied with sand production. In recent years, the sand control customization technology and single well precise design based on single formation was developed to optimize deliverability of single formation. The optimized sand control technique has become one of the major methods for increasing and stabilizing production in Bohai Bay offshore oilfield. This paper begins with an introduction of the application and limitations of traditional sand control methods in Bohai Bay offshore oilfield. Then, to meet the requirement of further enhancing oil recovery, sand management and proper sand control completion technology are introduced. After the experiment was conducted, the results of sand control were obtained from most oilfields. However, bad results of application still existed in some oilfields.

Unconventional field production relies heavily on artificial lift, but with reservoir energy depleting, lifting hydrocarbons efficiently and economically is one of the challenging parts of field development. Traditional lift selection methods are insufficient for managing unconventional wells with high initial decline rates. Understanding how production behaves under various lift conditions is crucial because lift method timing and design are the most important considerations for optimizing well performance. In order to increase the value of unconventional oil and gas assets, this paper presents an artificial-lift timing and selection (ALTS) methodology that is based on a hybrid data-driven and physics-based workflow. Our formulation employs a reduced physics model that is based on identification of Dynamic Drainage Volume (DDV) using commonly measured data (flowback, daily production rates, and wellhead pressure) to calculate reservoir pressure depletion, transient productivity index (PI) and dynamic inflow performance relationship (IPR). Transient PI as the forecasting variable normalizes both surface pressure effects and takes phase behavior into account, reducing noise. For any bottom hole pressure condition, the PI-based forecasting method is used to predict future IPRs and, as a result, oil, water, and gas rates. The workflow calculates well deliverability under various artificial lift types and design parameters. The ALTS workflow was applied to real-world field cases involving wells flowing under various operating conditions to determine the best strategy for producing the well among several candidate scenarios. The results of transient PI and dynamic IPR provided valuable insights into how and when to select different AL systems. The workflow is run on a regular basis with ever-changing subsurface and wellbore conditions against each candidate scenario using different pump models and other operating parameters (pressure, speed etc.). The method was applied in hindcasting mode to several wells to evaluate lost production opportunity and validate the results. In some cases, the best recommendation was to use a different artificial lift system than the one used in the field to significantly improve long-term well performance. Furthermore, optimal artificial lift operating point recommendations for wells are made, including optimal gas lift rates for gas lifted wells, optimal pump unit selection and speed for ESP and SRP wells. The proposed method predicts future unconventional reservoir IPR consistently and allows for continuous evaluation of artificial lift timing and selection scenarios in unconventional reservoirs with multiple lift types and designs. This has the potential to shift incumbent practices based on broad field heuristics, which are frequently ad hoc, inefficient, and manually intensive, toward well-specific ALTS analysis to improve field economics. Continuous use of this process has been shown to improve production, reduce deferred production, and extend the life of lift equipment.

Artificial lift is the backbone of unconventional field production. Lifting oil and gas in an optimal manner and economically is one of the most challenging phases of field development with depleting reservoir energy. Traditional approaches of lift selection are not sufficient to manage unconventional wells effectively, with high decline rates initially. It is of prime interest to understand production behavior under different lift conditions since the decision on timing and design of lift method are crucial for optimizing the well performance. This paper presents an artificial-lift timing and selection (ALTS) methodology based on a hybrid data-driven and physics-based workflow to maximize the value of unconventional oil and gas assets. Our formulation employs a reduced physics model that is based on identification of Dynamic Drainage Volume (DDV) using commonly measured data (daily production rates and wellhead pressure) to calculate reservoir pressure depletion, transient productivity index (PI) and dynamic inflow performance relationship (IPR). Transient PI as the forecasting variable allows normalizing both surface pressure effects and considers phase behavior, thus reducing noise. The PI-based forecasting method is used to predict future IPRs and subsequently oil, water, and gas rates for any bottom hole pressure condition. The workflow allows estimating well deliverability under different artificial lift types and design parameters. The ALTS workflow was applied to real field cases for wells flowing under different operating conditions to optimize the best strategy to produce the well amongst several candidate scenarios. Transient PI and dynamic IPR results provided valuable insights on how and when to select different AL systems. The workflow is run periodically with everchanging subsurface and wellbore conditions against each candidate scenario with various pump models and other operating parameters (pressure, speed etc.). The method was applied to several wells in a hindcasting mode to evaluate lost production opportunity and validate the results. In certain cases, the optimal recommendation pointed to selecting a different artificial lift system than the chosen method in the field to significantly improve long term well performance. In addition, optimal artificial lift operating point recommendations are made for wells including optimal gas lift rates for gas lifted wells, optimal pump unit selection and speed for wells on ESP and SRP. The proposed method allows predicting future unconventional reservoir IPR consistently and has allowed continuous evaluation of artificial lift timing and selection scenarios for multiple lift types and designs in unconventional reservoirs. This can transform incumbent practices based on broad field heuristics, which are often ad hoc, inefficient, and manually intensive, towards well-specific ALTS analysis to improve field economics. Continuous application of this process is shown to improve production, reduce deferred production and increase life of lift equipment.

Traditional DOF solutions use a web portal or intranet dashboard as the primary means of data access and collaboration. For an engineer these portals are useful to help identify field problems, reduce the amount of time spent looking for data and enable quicker analysis of issues. However, these platforms do not offer the rich two-way communication environment that could enable real-time interaction between engineers, systems, and support functions. Web portals are generally loosely coupled to modelling and visualization solutions which together can adversely affect performance of the solution. This paper presents how a new approach was tried, tested and proven to offer a transformation in the way we interact with well production, model based analytics and collaborate with others. This paper provides a case study documenting how a foundational Digital Oilfield (DOF) platform was extended to include model based workflows and multi channel mobile device integration.